poli 🧪: a library of discrete objective functions#
-poli is a library of discrete objective functions for benchmarking optimization algorithms. Examples include:
+poli
🧪: a library of discrete objective functions#
+poli
is a library of discrete objective functions for benchmarking optimization algorithms. If offers
-
-
🔬 stability of mutations from a wildtype protein (using foldx or rasp).
-🧪 docking scores of ligands to proteins (using dockstring, pyscreener and pytdc).
-💊 druglikeness or synthetic acccesibility of small molecules (using rdkit and pytdc).
-
Some of poli
’s features:
-
-
🔲 isolation of black box function calls inside conda environments. Don’t worry about clashes w. black box requirements, poli will create the relevant conda environments for you.
-🗒️ logging each black box call using observers.
+isolation of black box function calls inside conda environments. Don’t worry about clashes w. black box requirements, poli will create the relevant conda environments for you.
+logging each black box call using observers.
A numpy interface. Inputs are
np.array
s of strings, outputs arenp.array
s of floats.
-SMILES
andSELFIES
support for small molecule manipulation.
This documentation also discusses poli-baselines
, a collection of optimizers of these discrete black box functions.
We also provide poli-baselines
, a collection of optimizers of these discrete black box functions.
We are running a benchmark!
Using poli
and poli-baselines
, we are running a benchmark comparing high-dimensional Bayesian optimization algorithms for discrete sequence.
Discrete
+LaMBO2
+Optimizing protein sequences using guided discrete diffusion
+
+
Optimizing protein sequences using guided discrete diffusion
+Papenmeier et al’s Bounce, using their official implementation.
poli-baselines
", "Getting started", "poli \ud83e\uddea: a library of discrete objective functions", "Installing foldx
", "Computing the energy of a protein and a single mutation", "Rapid Stability Predictions", "Albuterol Similarity (using TDC)", "All objective functions", "Aloha objective function", "Amlodipine MPO (using TDC)", "Celecoxib Rediscovery (using TDC)", "Deco Hop (using TDC)", "dockstring", "DRD2 Docking (using TDC)", "DRD3 docking (using TDC)", "Ehrlich functions", "Fexofenadine MPO (using TDC)", "Protein (RFP) stability and SASA (using foldx
,lambo
)", "Protein solvent accessibility (using foldx
)", "Protein stability (using foldx
)", "GSK3\u03b2 (using TDC)", "Isomer C7H8N2O2 (using TDC)", "Isomer C9H10N2O2PF2Cl (using TDC)", "c-Jun N-terminal Kinases-3 (using TDC)", "Median 1 (using TDC)", "Median 2 (using TDC)", "Mestranol Similarity (using TDC)", "Osimetrinib MPO (using TDC)", "Penalized logP (using lambo
)", "Ranolazine MPO (using TDC)", "Log-solubility (logP)", "Quantitative Estimate of Druglikeness (QED)", "Synthetic Accessibility (using TDC)", "Scaffold Hop (using TDC)", "Sitagliptin MPO (using TDC)", "Optimizing jumps in Super Mario Bros", "Objective function name", "Thiothixene Rediscovery (using TDC)", "Toy continuous objective functions", "Troglitazone Rediscovery (using TDC)", "Valsartan SMARTS (using TDC)", "White Noise objective function", "Zaleplon MPO (using TDC)", "Registering and running observers in isolated processes", "Optimizing protein stability using random mutations", "Defining a problem solver in poli_baselines
", "Defining an observer", "Diving deeper: how does poli work under the hood?", "What is poli?", "Isolating black box objective functions", "Adaptive Linear Embedding Bayesian Optimization (ALEBO)", "Adaptively expanding subspaces (BAxUS)", "Bayesian Optimization", "Bounce", "CMA-ES", "Graph Genetic Algorithms", "Hvarfner\u2019s Vanilla Bayesian Optimization", "Latent Space Bayesian Optimization", "Line Bayesian Optimization", "Discrete NSGA-2", "Probabilistic Reparametrization", "Random mutations", "Sparse Axis-Aligned Subspaces Bayesian Optimization (SAASBO)", "Template: optimization algorithm", "Trust Region Bayesian Optimization (Turbo)"], "terms": {"librari": [0, 39, 45, 306, 396, 438, 440, 441], "discret": [0, 21, 117, 118, 119, 146, 179, 390, 391, 392, 397, 398, 400, 401, 402, 403, 404, 405, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 429, 430, 431, 432, 433, 434, 436, 437, 438, 440, 442, 443, 444, 445, 448, 450, 452, 454, 456], "black": [0, 3, 4, 5, 6, 11, 12, 13, 14, 19, 20, 21, 24, 25, 26, 46, 78, 109, 111, 112, 117, 118, 119, 132, 133, 142, 146, 152, 153, 158, 162, 163, 165, 169, 170, 172, 175, 176, 210, 220, 246, 256, 257, 260, 261, 265, 266, 268, 270, 279, 325, 336, 337, 371, 392, 395, 397, 398, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 429, 430, 431, 432, 434, 436, 437, 438, 440, 444, 451], "box": [0, 3, 4, 5, 6, 11, 12, 13, 14, 19, 20, 21, 24, 25, 26, 46, 78, 109, 111, 112, 117, 118, 119, 132, 133, 142, 146, 152, 153, 158, 162, 163, 165, 169, 170, 172, 175, 176, 210, 220, 246, 256, 257, 260, 261, 265, 266, 268, 270, 279, 325, 336, 337, 371, 392, 395, 397, 398, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 429, 430, 431, 432, 434, 436, 437, 438, 440, 444, 451], "object": [0, 3, 4, 5, 6, 14, 19, 20, 25, 26, 39, 55, 59, 64, 65, 74, 81, 88, 89, 107, 109, 110, 111, 112, 116, 117, 118, 129, 132, 133, 138, 142, 152, 153, 162, 163, 165, 169, 170, 175, 176, 210, 220, 229, 246, 260, 265, 267, 270, 277, 278, 279, 301, 306, 322, 324, 325, 328, 336, 369, 371, 376, 390, 391, 392, 393, 397, 398, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 429, 431, 432, 434, 437, 438, 442, 443, 444, 445, 448, 450, 451, 452, 454, 456], "function": [0, 3, 4, 5, 6, 14, 15, 19, 20, 21, 28, 39, 42, 43, 44, 45, 47, 51, 52, 57, 58, 60, 62, 63, 65, 69, 73, 76, 77, 82, 85, 86, 100, 107, 109, 111, 112, 116, 117, 118, 129, 132, 133, 138, 142, 146, 152, 153, 158, 163, 165, 170, 176, 181, 203, 210, 244, 245, 246, 260, 261, 265, 266, 267, 279, 289, 301, 305, 306, 322, 324, 325, 327, 328, 336, 337, 346, 356, 359, 369, 371, 373, 376, 380, 383, 390, 391, 392, 393, 396, 397, 398, 401, 402, 403, 404, 405, 406, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 429, 431, 432, 434, 437, 438, 444, 450, 451], "modul": [0, 1, 3, 6, 7, 12, 24, 25, 28, 37, 41, 43, 44, 46, 56, 57, 60, 62, 63, 66, 72, 73, 78, 79, 80, 82, 86, 93, 99, 112, 113, 116, 120, 123, 126, 129, 132, 135, 138, 144, 146, 147, 150, 158, 165, 172, 178, 198, 200, 212, 218, 221, 224, 227, 230, 233, 236, 239, 242, 244, 245, 248, 251, 254, 256, 257, 258, 263, 268, 271, 275, 281, 284, 287, 302, 305, 329, 332, 335, 339, 343, 344, 355, 357, 358, 363, 365, 369, 371, 375, 379, 383, 385, 388], "class": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 18, 19, 20, 21, 25, 26, 27, 38, 39, 40, 53, 54, 55, 63, 64, 68, 70, 80, 81, 115, 117, 118, 119, 122, 125, 128, 131, 132, 133, 134, 137, 141, 142, 143, 146, 149, 152, 153, 161, 162, 163, 164, 168, 169, 170, 171, 174, 175, 176, 177, 179, 180, 196, 197, 198, 199, 200, 202, 203, 204, 205, 209, 210, 211, 214, 215, 217, 220, 223, 226, 229, 232, 235, 238, 241, 244, 245, 246, 247, 250, 253, 256, 257, 260, 261, 262, 265, 266, 267, 270, 273, 274, 277, 278, 279, 280, 283, 286, 301, 304, 324, 325, 326, 327, 328, 331, 334, 336, 337, 338, 341, 391, 392, 407, 437, 438], "util": [1, 28, 200, 289, 391, 438, 441, 442, 444, 448, 454], "insid": [1, 6, 16, 112, 138, 391, 393, 394, 396, 397, 406, 427, 430, 436, 440, 441], "thi": [3, 5, 6, 7, 8, 11, 15, 19, 20, 21, 25, 28, 39, 44, 45, 51, 52, 57, 58, 60, 64, 65, 73, 77, 79, 80, 81, 82, 85, 86, 109, 111, 115, 117, 122, 125, 128, 132, 137, 138, 143, 146, 149, 162, 169, 175, 196, 200, 220, 223, 226, 229, 232, 235, 238, 241, 244, 245, 250, 253, 256, 257, 260, 265, 270, 283, 286, 304, 306, 322, 324, 325, 331, 334, 336, 341, 369, 371, 376, 380, 383, 391, 392, 393, 394, 395, 396, 397, 398, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 428, 429, 430, 431, 432, 433, 434, 436, 437, 438, 440, 441, 442, 443, 444, 445, 446, 448, 450, 451, 452, 453, 454, 455, 456], "implement": [3, 5, 6, 7, 11, 12, 13, 15, 19, 20, 21, 24, 25, 44, 80, 115, 118, 122, 125, 128, 132, 133, 137, 141, 146, 149, 163, 170, 176, 179, 196, 204, 220, 223, 226, 229, 232, 235, 238, 241, 242, 244, 245, 250, 253, 256, 257, 274, 277, 278, 279, 283, 286, 304, 325, 331, 334, 341, 391, 392, 394, 399, 404, 407, 409, 415, 420, 430, 436, 437, 440, 441, 442, 443, 444, 445, 446, 448, 451, 452, 454, 456], "abstract": [3, 4, 6, 7, 8, 13, 14, 21, 38, 39, 390, 391, 436, 442, 456], "from": [3, 26, 45, 51, 52, 56, 57, 58, 59, 60, 61, 80, 81, 85, 93, 111, 115, 122, 125, 128, 138, 149, 163, 170, 176, 182, 187, 190, 205, 223, 226, 232, 235, 238, 241, 250, 253, 256, 257, 283, 286, 292, 295, 304, 306, 312, 321, 331, 334, 341, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 436, 437, 438, 440, 441, 442, 443, 444, 445, 446, 448, 450, 451, 452, 453, 454, 455, 456], "which": [3, 4, 6, 9, 14, 21, 26, 54, 61, 81, 85, 89, 110, 132, 142, 246, 265, 267, 279, 324, 325, 337, 391, 392, 396, 397, 399, 406, 407, 409, 424, 427, 430, 436, 437, 438, 440, 441, 444, 448, 454], "all": [3, 13, 107, 109, 112, 117, 118, 119, 138, 196, 306, 337, 391, 392, 393, 396, 397, 400, 407, 436, 437, 440, 441], "should": [3, 64, 138, 146, 391, 393, 395, 398, 400, 401, 402, 403, 404, 405, 406, 407, 408, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 429, 430, 431, 432, 433, 434, 436, 437, 438, 441], "inherit": [3, 20, 115, 122, 125, 128, 149, 223, 226, 232, 235, 238, 241, 250, 253, 283, 286, 304, 331, 334, 341, 392, 436, 437, 438], "info": [26, 142, 337, 392, 436, 437, 438, 440, 445, 451, 452], "problemsetupinform": [26, 39, 337, 409, 438], "batch_siz": [4, 5, 14, 20, 26, 109, 118, 133, 142, 163, 170, 176, 192, 210, 246, 261, 266, 279, 325, 337, 391, 407, 451], "option": [4, 5, 14, 20, 26, 48, 50, 51, 52, 55, 58, 61, 74, 81, 83, 84, 87, 88, 89, 101, 102, 109, 111, 118, 133, 142, 163, 170, 176, 210, 246, 261, 266, 279, 325, 328, 337, 392, 407], "int": [4, 5, 14, 20, 26, 39, 45, 65, 77, 83, 84, 101, 102, 109, 111, 118, 133, 142, 163, 170, 176, 197, 210, 246, 261, 266, 279, 290, 291, 294, 325, 328, 337, 391, 392, 407, 437, 438, 440, 445, 452], "none": [4, 5, 14, 20, 26, 39, 51, 52, 55, 58, 74, 81, 101, 102, 109, 111, 118, 133, 142, 163, 170, 176, 191, 193, 195, 210, 211, 246, 261, 266, 279, 325, 328, 337, 391, 392, 398, 400, 401, 402, 403, 404, 405, 407, 408, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 428, 429, 430, 431, 432, 433, 434, 438], "parallel": [4, 5, 14, 26, 109, 118, 133, 142, 163, 170, 176, 210, 246, 261, 266, 279, 325, 337, 391, 407], "bool": [4, 5, 14, 26, 48, 49, 50, 51, 52, 58, 81, 83, 84, 87, 88, 89, 109, 111, 118, 133, 142, 163, 170, 176, 188, 210, 246, 261, 266, 279, 322, 325, 337, 391, 407], "fals": [4, 5, 14, 26, 49, 50, 51, 52, 58, 81, 84, 87, 88, 89, 109, 111, 118, 133, 142, 163, 170, 176, 187, 188, 194, 210, 246, 261, 266, 279, 295, 325, 337, 391, 397, 407, 427, 433, 438, 440], "num_work": [4, 5, 14, 26, 109, 118, 133, 142, 163, 170, 176, 210, 246, 261, 266, 279, 325, 337, 391, 407], "evaluation_budget": [4, 5, 14, 26, 109, 118, 133, 142, 163, 170, 176, 210, 246, 261, 266, 279, 325, 337, 391, 407], "inf": [4, 5, 13, 14, 26, 109, 115, 118, 122, 125, 128, 133, 142, 149, 163, 170, 176, 210, 223, 226, 232, 235, 238, 241, 246, 250, 253, 261, 266, 279, 283, 286, 304, 325, 331, 334, 337, 341, 391, 407, 433, 440], "base": [4, 8, 18, 39, 64, 138, 204, 390, 393, 394, 396, 399, 424, 442, 443, 445, 448, 451, 452, 454, 456], "optim": [4, 6, 13, 21, 39, 80, 125, 146, 150, 242, 244, 245, 304, 305, 306, 324, 327, 331, 376, 390, 395, 396, 399, 402, 405, 407, 409, 410, 411, 412, 413, 414, 415, 416, 417, 420, 429, 430, 431, 437, 438, 443, 445, 451, 452, 453], "problem": [4, 7, 8, 11, 13, 14, 26, 28, 42, 72, 73, 109, 117, 118, 119, 133, 134, 143, 164, 177, 260, 265, 267, 268, 280, 287, 306, 324, 325, 335, 336, 337, 376, 393, 396, 397, 398, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 429, 431, 432, 433, 434, 438, 440, 441, 443, 445, 451, 452, 453], "paramet": [4, 5, 8, 14, 20, 26, 45, 48, 49, 50, 51, 52, 55, 58, 59, 61, 64, 65, 74, 77, 81, 82, 83, 84, 85, 87, 88, 89, 101, 102, 109, 111, 118, 119, 133, 134, 142, 163, 164, 170, 176, 177, 210, 246, 261, 266, 267, 279, 280, 325, 337, 392, 407], "The": [4, 5, 11, 14, 20, 21, 25, 26, 39, 45, 55, 58, 59, 61, 64, 65, 74, 77, 80, 81, 83, 84, 85, 87, 88, 89, 109, 110, 111, 116, 117, 118, 119, 133, 138, 142, 162, 163, 169, 170, 175, 176, 204, 210, 242, 246, 261, 266, 267, 270, 271, 274, 279, 301, 320, 321, 324, 325, 336, 337, 390, 392, 394, 396, 397, 399, 404, 405, 407, 409, 410, 411, 412, 427, 436, 437, 441, 442, 443, 448, 451, 454, 456], "setup": [21, 26, 55, 77, 143, 337, 409], 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401, 402, 403, 405, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 421, 425, 426, 429, 431, 432, 434, 444, 450], "flag": [4, 5, 14, 26, 118, 133, 142, 163, 170, 176, 210, 246, 261, 266, 279, 325], "indic": [4, 5, 14, 26, 118, 133, 142, 163, 170, 176, 182, 183, 210, 246, 261, 266, 279, 325, 392, 437], "whether": [4, 5, 11, 14, 21, 26, 58, 87, 88, 118, 133, 142, 163, 170, 176, 210, 246, 261, 266, 279, 325, 337, 371, 383, 391, 396, 397, 407, 422, 423, 427, 436, 438], "number": [4, 5, 14, 21, 26, 45, 65, 77, 99, 100, 101, 102, 109, 111, 118, 133, 142, 163, 170, 176, 210, 246, 261, 266, 279, 301, 325, 337, 392, 398, 399, 401, 402, 403, 404, 405, 407, 408, 412, 413, 414, 415, 416, 417, 418, 419, 421, 424, 425, 426, 427, 429, 431, 432, 434, 437, 440, 448], "worker": [4, 5, 14, 26, 118, 133, 142, 163, 170, 176, 210, 246, 261, 266, 279, 325, 337], "us": [4, 5, 6, 9, 11, 14, 21, 26, 39, 40, 51, 52, 55, 58, 60, 64, 65, 79, 80, 81, 109, 111, 115, 117, 118, 122, 125, 128, 133, 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49, 50, 51, 52, 55, 58, 59, 65, 77, 80, 81, 83, 84, 85, 89, 101, 102, 109, 111, 220, 322, 325, 391, 392, 393, 394, 395, 396, 397, 398, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 438, 440, 441, 442, 443, 445, 448, 450, 452, 454, 456], "you": [5, 6, 14, 81, 89, 115, 122, 125, 128, 137, 138, 142, 149, 220, 223, 226, 229, 232, 241, 250, 253, 261, 266, 279, 283, 286, 304, 306, 331, 334, 341, 391, 392, 393, 394, 395, 396, 397, 398, 400, 401, 402, 403, 404, 405, 407, 408, 409, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 436, 437, 438, 440, 441, 442, 443, 445, 448, 450, 452, 454, 456], "construct": [5, 205, 407], "maxim": [5, 128, 146, 301, 306, 321, 322, 392, 397, 407, 430], "minim": [5, 306, 391, 392, 399, 404, 437, 451], "latter": [5, 396], "onli": [5, 6, 111, 179, 187, 196, 315, 319, 320, 325, 392, 393, 396, 397, 404, 409, 410, 411, 430, 437], "differ": [5, 25, 83, 162, 169, 175, 270, 391, 392, 393, 396, 397, 437, 438, 441, 442, 443, 445, 448, 452, 454], "return": [5, 26, 48, 49, 50, 51, 52, 59, 61, 65, 74, 81, 83, 84, 85, 87, 88, 89, 109, 110, 111, 170, 184, 185, 186, 189, 191, 206, 207, 246, 260, 261, 265, 266, 267, 293, 335, 336, 337, 391, 392, 397, 400, 404, 407, 409, 410, 411, 422, 423, 427, 433, 436, 437], "instead": [5, 306, 392, 410, 411, 437, 448], "same": [5, 84, 85, 242, 244, 245, 246, 391, 393, 397, 416, 417], "factori": [7, 8, 25, 64, 107, 109, 111, 112, 116, 117, 119, 129, 132, 134, 143, 152, 153, 162, 164, 165, 169, 175, 177, 260, 265, 267, 270, 280, 301, 324, 336, 391, 394, 407], "defin": [8, 82, 268, 306, 376, 392, 430, 440, 441, 445, 452], "interfac": [8, 80, 107, 391, 394, 412, 415, 432, 441, 445, 448, 452], "instanc": [8, 14, 20, 119, 134, 142, 164, 177, 267, 280, 325, 391, 392, 393, 436, 441, 451], "get_setup_inform": 391, "specifi": [8, 14, 58, 59, 109, 119, 134, 142, 164, 177, 185, 190, 198, 204, 267, 279, 280, 324, 396, 422, 423, 442, 451], "metaclass": 9, "abstractproblemfactori": [9, 391], "overrid": 9, "__repr__": 9, "__str__": 9, "arg": [9, 198, 199, 204, 205, 391, 396], "kwarg": [9, 198, 199, 204, 205, 391], "contain": [6, 11, 12, 24, 28, 45, 60, 73, 79, 82, 83, 84, 86, 110, 182, 183, 200, 328, 380, 391, 393, 396, 407, 410, 411, 427], "relat": [12, 13, 24, 46, 78, 109, 395, 436], "around": [15, 40, 138, 150, 260, 265, 275, 394, 399, 451], "therapeut": [13, 14, 15, 115, 122, 125, 128, 137, 141, 149, 220, 223, 226, 229, 232, 235, 238, 241, 250, 253, 277, 278, 283, 286, 304, 331, 334, 341, 390, 394, 398, 399, 401, 402, 403, 405, 406, 408, 412, 413, 414, 415, 416, 417, 418, 419, 421, 424, 425, 426, 429, 431, 432, 434], "data": [14, 15, 43, 58, 118, 133, 138, 187, 188, 194, 195, 200, 220, 229, 390, 394, 396, 398, 399, 401, 402, 403, 405, 406, 408, 409, 412, 413, 414, 415, 416, 417, 418, 419, 421, 424, 425, 426, 429, 431, 432, 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434, 436, 442, 443, 444, 445, 446, 448, 450, 451, 452, 454, 456], "octob": 15, "2022": [13, 14, 15, 80, 115, 122, 125, 128, 132, 133, 134, 137, 141, 149, 150, 220, 223, 226, 229, 232, 235, 238, 241, 242, 244, 245, 250, 253, 277, 278, 283, 286, 304, 331, 334, 341, 390, 395, 396, 399, 404, 405, 409, 410, 411, 412, 415, 420, 436, 443, 452], "1033": [13, 14, 15, 115, 122, 125, 128, 137, 141, 149, 220, 223, 226, 229, 232, 235, 238, 241, 250, 253, 277, 278, 283, 286, 304, 331, 334, 341], "36": 15, "http": [13, 14, 15, 45, 57, 58, 80, 82, 115, 122, 125, 128, 132, 133, 134, 137, 138, 141, 146, 149, 150, 220, 223, 226, 229, 232, 235, 238, 241, 242, 244, 245, 250, 253, 256, 257, 271, 274, 277, 278, 283, 286, 304, 306, 327, 328, 331, 334, 341, 390, 391, 392, 393, 394, 395, 396, 397, 398, 400, 401, 402, 403, 404, 405, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 429, 430, 431, 432, 433, 434, 436, 440, 442, 443, 445, 448, 450, 452, 454, 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445, 452], "from_smil": [142, 261, 266], "true": [48, 49, 50, 51, 52, 58, 81, 83, 84, 89, 109, 111, 142, 188, 205, 210, 246, 261, 266, 322, 391, 396, 410, 411, 433, 436, 438, 440], "kwargs_for_oracl": 14, "repres": [26, 65, 83, 88, 142, 204, 440], "It": [64, 65, 138, 391, 392, 393, 396, 404, 406, 437, 440, 442], "name": [11, 14, 15, 55, 58, 59, 77, 87, 88, 89, 109, 111, 133, 142, 325, 328, 391, 392, 393, 396, 397, 409, 433, 436, 438, 440, 443, 445, 452], "comput": [25, 61, 81, 83, 85, 118, 133, 138, 162, 163, 169, 170, 172, 175, 176, 242, 244, 245, 258, 261, 266, 268, 270, 306, 390, 393, 394, 395, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 429, 430, 431, 432, 433, 434, 441, 442, 443, 445, 448, 450, 452, 454, 456], "dock": [132, 133, 134, 137, 138, 141, 142, 143, 390, 394, 399, 404, 441], "score": [132, 133, 134, 163, 170, 176, 390, 394, 397, 399, 404, 405, 406, 410, 411, 420, 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261, 266, 270, 274, 322, 337, 371, 390, 392, 394, 396, 397, 404, 407, 411, 433, 437, 438], "repo": [58, 138, 393], "download_path": 58, "branch": [58, 59, 393], "sha": [58, 59], "commit": [58, 59, 397], "exists_ok": 58, "parent": [58, 396, 397, 438, 451], "thei": [21, 58, 80, 146, 244, 245, 246, 396, 407, 420, 438, 448], "do": [58, 138, 244, 245, 391, 392, 396, 409, 420, 427, 437, 441], "except": [58, 65, 77], "github_token_for_poli": [58, 409], "doe": [53, 58, 244, 245], "try": [58, 410, 411], "without": [58, 369, 391, 392, 411, 437], "rate": 58, "limit": [58, 328, 397, 409], "60": 58, "request": [58, 396], "per": 58, "hour": 58, "anonym": [58, 409], "To": [58, 391, 393, 394, 395, 396, 420], "token": [58, 193, 261, 266, 291, 293, 391, 409, 427, 453, 455], "like": [58, 111, 138, 315, 390, 391, 392, 395, 406, 424, 427, 430, 437, 438, 440, 441, 442, 443, 444, 448, 454, 456], "follow": [11, 21, 58, 79, 391, 392, 393, 395, 396, 397, 398, 400, 401, 402, 403, 404, 405, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 436, 437, 440, 442, 443, 445, 448, 450, 452, 454, 456], "instruct": [58, 391, 392, 440], "here": [58, 391, 392, 394, 396, 397, 409, 413, 414, 426, 434, 436, 441, 442, 443, 445, 448, 451, 452, 454], "en": [58, 327, 398, 401, 402, 403, 404, 405, 408, 412, 413, 414, 415, 416, 417, 418, 419, 421, 424, 425, 426, 429, 431, 432, 434, 436, 440, 443, 450, 454], "authent": [58, 64, 65], "keep": [58, 85, 301, 410, 411, 448], "your": [6, 58, 138, 392, 395, 396, 397, 406, 409, 410, 411, 428, 438, 441], "account": 58, "secur": 58, "person": [58, 409], "fine": [58, 393], "grain": 58, "get_repo": 59, "release_2023_09": 59, "068441957858f786c227825d90eb2c43f4f2b000": 59, "internet": 60, "reproduc": 60, "purpos": 60, "make": [60, 301, 380, 393, 395, 396, 397, 409, 427, 451], "sure": [60, 393, 395, 396, 397, 409, 427, 438], "malici": 60, "being": [60, 301, 395, 397, 409], "filepath": 61, "read_mod": 61, "rb": 61, "md5": [61, 397], "hex": 61, "digest": 61, "open": [61, 256, 257, 390, 396, 422, 423, 438, 443], "binari": [61, 81, 138, 163, 170, 176, 396, 409, 410, 411], "form": [11, 61, 146, 390, 394, 396, 407], "mode": [61, 82], "read": [61, 81, 195, 396, 413, 414, 426, 434, 438], "hex_digest": 61, "txt": [61, 81, 85, 391, 395, 396, 409, 410, 411, 438], "d41d8cd98f00b204e9800998ecf8427": 61, "wrap": [62, 63], "interprocess": [62, 63], "commun": [6, 62, 63, 64, 391, 422, 423], "kwargs_for_factori": [64, 109, 111], "connect": [64, 65, 77, 204], "inter": 64, "These": [64, 146, 391, 394, 396, 397, 399, 407, 409, 430, 436, 438, 440], "inner": [64, 371], "up": [64, 65, 392, 404, 406, 427, 437, 438], "listen": 64, "random": [64, 99, 100, 101, 102, 109, 111, 146, 392, 394, 405, 407, 412, 415, 437, 440, 442, 443, 444, 446, 448, 450, 451, 454, 455, 456], "subprocess": [64, 396], "expect": [6, 64, 80, 81, 82, 85, 138, 163, 170, 176, 371, 391, 392, 394, 396, 397, 398, 400, 401, 402, 403, 404, 405, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 429, 430, 431, 432, 433, 434, 438, 442, 443, 445, 448, 450, 452, 454, 456], "well": [64, 393, 396, 404, 441, 444], "other": [14, 64, 115, 261, 266, 391, 392, 395, 436, 438, 441], "objective_factori": [64, 369, 391, 393, 409, 428, 438, 440, 443, 445, 452], "determin": 64, "client": 65, "get": [65, 76, 89, 371, 391, 392, 396, 399, 413, 414, 426, 434, 437, 438, 440], "eoferror": 65, "host": 65, "readi": [65, 188], "yet": [65, 391, 437], "connectionrefusederror": 65, "refus": 65, "attempt": 65, "establish": [65, 77], "retri": 65, "befor": [65, 74, 392, 436], "conda_environ": 74, "cwd": [74, 396], "activ": [74, 391, 393, 394, 409, 438, 442, 443, 445, 448, 452, 454], "ad": [74, 138, 369, 406, 438], "current": [74, 413, 414, 426, 428, 434, 440, 441], "either": [14, 133, 142, 279, 396, 397, 407, 409, 428, 438, 450], "mother": [76, 77], "receiv": 77, "wait": 77, "occur": 77, 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122, 125, 128, 137, 141, 146, 149, 220, 223, 226, 229, 232, 235, 238, 241, 250, 253, 260, 277, 278, 283, 286, 304, 318, 331, 334, 341, 390, 407, 438], "m": [79, 137, 193, 256, 257, 390, 396, 397, 398, 400, 401, 402, 403, 404, 405, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 429, 430, 431, 432, 433, 434, 438, 442, 443, 445, 448, 450, 452, 454, 456], "p": [79, 390, 407, 438], "v": [79, 81, 407, 438], "measur": [80, 115, 187, 238, 396, 422, 423], "stabil": [80, 81, 165, 169, 170, 172, 175, 256, 257, 268, 270, 390, 393, 394, 396, 399, 410, 441], "sasa": [80, 81, 158, 162, 163, 164, 172, 175, 176, 177, 268, 270, 394, 399, 410, 411], "foldxinterfac": 80, "queri": [80, 146, 391, 392, 397, 398, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 437], "heavili": [6, 80, 396], "inspir": [80, 396], "wai": [21, 80, 392, 393, 396, 407, 437, 438, 440, 441], "lambo": [80, 138, 150, 242, 244, 245, 246, 394, 396, 399, 410, 411, 436], "automat": [80, 111, 390, 391, 395], "acceler": [80, 150, 242, 244, 245, 390, 409, 410, 411, 420], "bayesian": [80, 150, 242, 244, 245, 376, 390, 392, 394, 409, 410, 411, 420, 437, 443, 445, 452], "biolog": [80, 150, 242, 244, 245, 390, 409, 410, 411, 420], "design": [13, 80, 115, 122, 125, 128, 132, 133, 134, 137, 149, 150, 204, 220, 223, 226, 229, 232, 235, 238, 241, 242, 244, 245, 250, 253, 283, 286, 304, 331, 334, 341, 390, 391, 392, 394, 395, 398, 399, 401, 402, 403, 404, 405, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 425, 426, 429, 431, 432, 434, 436, 437, 440], "withdenois": 80, "autoencod": [80, 150, 204, 242, 244, 245, 390, 409, 410, 411, 420, 442, 443, 444, 448, 454, 456], "stanton": [80, 146, 150, 242, 244, 245, 390, 395, 396, 407, 409, 410, 411, 420, 436], "samuel": [80, 150, 242, 244, 245, 390, 407, 409, 410, 411, 420, 452], "weslei": [80, 150, 242, 244, 245, 390, 409, 410, 411, 420], "maddox": [80, 150, 242, 244, 245, 390, 409, 410, 411, 420], "nate": [80, 150, 242, 244, 245, 390, 409, 410, 411, 420], "gruver": [80, 150, 242, 244, 245, 390, 409, 410, 411, 420], "phillip": [80, 150, 242, 244, 245, 390, 409, 410, 411, 420], "maffetton": [80, 150, 242, 244, 245, 390, 409, 410, 411, 420], "emili": [80, 150, 242, 244, 245, 390, 409, 410, 411, 420], "delanei": [80, 150, 242, 244, 245, 390, 409, 410, 411, 420], "peyton": [80, 150, 242, 244, 245, 390, 409, 410, 411, 420], "greensid": [80, 150, 242, 244, 245, 390, 409, 410, 411, 420], "andrew": [80, 150, 242, 244, 245, 390, 407, 409, 410, 411, 420], "gordon": [80, 150, 242, 244, 245, 390, 409, 410, 411, 420], "wilson": [80, 150, 242, 244, 245, 390, 409, 410, 411, 420], "arxiv": [13, 80, 146, 150, 242, 244, 245, 390, 407, 409, 410, 411, 420, 445, 448], "juli": [80, 150, 242, 244, 245, 390, 409, 410, 411, 412, 415, 420], "ab": [13, 80, 146, 150, 242, 244, 245, 390, 407, 409, 410, 411, 420, 445, 448], "2203": [80, 150, 242, 244, 245, 390, 409, 410, 411, 420], "12742": [80, 150, 242, 244, 245, 390, 409, 410, 411, 420], "samuelstanton": [80, 138, 409], "working_dir": [81, 396], "interact": [81, 396], "engin": [81, 306, 430, 440, 444], "softwar": [81, 395, 397, 398, 400, 401, 402, 403, 404, 405, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 429, 430, 431, 432, 433, 434, 442, 443, 445, 448, 450, 452, 454, 456], "where": [81, 117, 118, 119, 205, 391, 392, 394, 396, 427, 437, 441, 442, 443, 445, 448, 452, 454], "store": [81, 392, 397, 436, 441], "pdb_file": 81, "remove_and_renam": 81, "ph": [81, 396, 410, 411], "remove_heteroatom": 81, "_repair_if_necessary_and_provide_path": 81, "_simulate_mut": 81, "_read_energi": 81, "_compute_sasa": 81, "solvent": [81, 158, 163, 164, 172, 176, 177, 268, 390, 394, 396, 399], "surfac": [81, 158, 163, 164, 172, 176, 177, 268, 394, 396, 399, 410], "area": [81, 158, 163, 164, 172, 176, 177, 268, 394, 396, 399, 410], "compute_st": 81, "structur": [81, 87, 88, 89, 138, 170, 392, 396, 406, 438], "compute_sasa": 81, "compute_stability_and_sasa": 81, "singl": [81, 85, 256, 257, 306, 390, 394, 397, 399, 420, 422, 423, 430, 436, 438, 444, 450], "copy_foldx_fil": 81, "copi": [81, 391, 392, 396, 437, 451], "write_mutations_to_fil": 81, "wildtype_resiud": 81, "output_dir": 81, "write": [81, 138, 371, 391, 393, 396, 406, 435, 438, 439, 447, 449], "stdout": 81, "previou": [81, 396, 438], "reli": [81, 369, 430, 448, 454], "rotabas": [81, 395, 396, 409, 410, 411], "longer": 81, "manipul": [82, 391, 393, 394], "accord": [82, 186, 396, 440], "certain": [82, 128, 146, 304, 331, 394, 396, 403, 404, 407, 425, 454], "first": [82, 83, 85, 391, 392, 394, 396, 399, 400, 436, 437], "letter": [82, 85, 117, 118, 119, 394, 396, 399, 400], "second": [82, 83, 85, 196, 394, 396, 399], "posit": [82, 83, 85, 185, 190, 392, 396, 397, 436, 437, 440], "third": [82, 85], "chain": [82, 85, 396, 397], "fourth": [82, 85], "mutant": [82, 84, 85, 256, 257, 392, 396, 437], "foldxsuit": [82, 395, 396], "crg": [82, 395, 396], "eu": [82, 395, 396], "string_1": 83, "string_2": 83, "edit": [83, 256, 257, 397], "oper": [83, 395], "assertionerror": 83, "abc": 83, "abd": 83, "def": [83, 391, 392, 437, 438], "wildtype_pdb_fil": [84, 410, 411], "mutated_residue_str": [84, 85], "return_hamming_dist": 84, "find": [84, 117, 118, 119, 138, 391, 397, 404, 406, 438, 440, 442, 443, 445, 448, 452, 454], "closest": 84, "ham": 84, "distanc": [84, 85, 118, 397, 400], "along": 84, "best": [84, 392, 436, 437, 446, 451], "candid": [84, 392, 440], "found": [84, 393, 396, 436, 440], "bio": [85, 88, 89, 396], "individual_list": [85, 396], "levenshtein": 85, "track": [85, 390, 438], "written": 85, "line": [85, 394, 396], "ea1r": 85, "still": [85, 441], "need": [11, 85, 138, 391, 392, 395, 396, 406, 409, 410, 411, 436, 438], "dummi": 85, "itself": [85, 391, 393], "ecd": 85, "acd": 85, "would": [85, 391, 392, 396, 397, 438], "ea1a": 85, "load": [86, 110, 179, 187, 196, 396, 438], "them": [86, 138, 376, 391, 392, 393, 396, 397, 399, 406, 437, 440, 445, 452], "path_to_pdb": [87, 88, 89], "structure_nam": [87, 88, 89], "done": [89, 138, 396, 409, 420, 440], "quietli": 89, "some": [89, 200, 246, 376, 392, 393, 394, 396, 409, 430, 438, 440], "get_structur": [89, 396], "pdbparser": [89, 396], "numpi": [101, 271, 292, 391, 393, 394, 398, 400, 401, 402, 403, 404, 405, 406, 408, 409, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 428, 429, 430, 431, 432, 433, 434, 438, 441, 442, 443, 444, 445, 448, 450, 451, 452, 453, 454, 455, 456], "obj": [], "dynam": 391, "intern": [390, 391, 412, 415, 424, 428, 450], "constructor": [], "factory_kwarg": [], "processwrapp": [], "how": [138, 391, 392, 393, 394, 396, 399, 436, 437, 438, 440], "objective_nam": [], "loop": [369, 390, 442, 443, 444, 448, 454, 456], "process_wrapp": [], "observer_init_info": 109, "force_regist": [], "force_isol": [4, 14, 109, 111, 133, 142, 163, 170, 176, 210, 246, 261, 279, 391, 407], "instantiant": 109, "associ": [109, 390, 436, 441, 442, 456], "caller": [109, 111, 438], "forward": [109, 111, 438], "logger": [109, 111], "ask": 392, "confirm": [], "By": [109, 261, 266, 325, 391, 392, 407, 410, 411, 444, 450], "onc": [109, 391, 392, 427, 440], "round": [109, 337], "down": 109, "give": [109, 290, 376, 391], "config": [110, 371], "configpars": 110, "just": [53, 111, 391, 409, 436, 438], "One": [111, 395, 440, 450], "aloha": [111, 393, 394, 399, 436, 437, 440, 451], "arrai": [111, 182, 183, 192, 193, 289, 292, 294, 391, 393, 394, 398, 400, 401, 402, 403, 404, 405, 406, 408, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 436, 438, 440, 441, 452], "reshap": [111, 392, 420, 422, 427, 442, 443, 444, 448, 450, 454, 456], "simpl": [117, 118, 119, 132, 133, 134, 186, 260, 261, 265, 266, 392, 436, 437, 440], "goal": [117, 118, 119, 301], "o": [117, 118, 119, 390, 398, 400, 401, 402, 403, 404, 405, 408, 412, 413, 414, 415, 416, 417, 418, 419, 421, 424, 425, 426, 429, 431, 432, 434, 438, 441, 448], "among": [115, 117, 118, 119, 395, 396], "poli__bas": [117, 324, 336], "yml": [117, 138, 324, 336, 392, 397, 409, 442, 443, 445, 448, 452, 454], "simultan": [14, 118, 133, 142, 261, 266, 279, 397], "map": [118, 133], "symbol": [118, 133], "main": [6, 118, 261, 266, 392, 393, 394, 395, 396, 437, 438], "api": [132, 133, 134, 393, 409], "assess": [132, 133, 134, 256, 257, 394, 399], "small": [132, 133, 134, 261, 391, 393, 404, 405, 406, 424, 438, 440], "protein": [132, 133, 134, 146, 162, 169, 170, 175, 256, 257, 270, 390, 393, 395, 397, 404, 406, 407, 440, 441], "easi": [132, 133, 134, 146, 390, 396, 404, 407, 440], "molecular": [13, 115, 122, 125, 128, 132, 133, 134, 137, 149, 162, 169, 175, 223, 226, 232, 235, 238, 241, 250, 253, 270, 283, 286, 304, 331, 334, 341, 390, 398, 401, 402, 403, 404, 405, 408, 412, 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396, 399, 413, 414, 426, 434, 437, 438, 440], "eoferror": 65, "host": 65, "readi": [65, 188], "yet": [65, 391, 437], "connectionrefusederror": 65, "refus": 65, "attempt": 65, "establish": [65, 77], "retri": 65, "befor": [65, 74, 392, 436], "conda_environ": 74, "cwd": [74, 396], "activ": [74, 391, 393, 394, 409, 438, 442, 443, 445, 448, 453, 455], "ad": [74, 138, 369, 406, 438], "current": [74, 413, 414, 426, 428, 434, 440, 441, 449], "either": [14, 133, 142, 279, 396, 397, 407, 409, 428, 438, 451], "mother": [76, 77], "receiv": 77, "wait": 77, "occur": 77, "sent": 77, "back": [77, 289], "quit": [77, 391, 397, 440], "messag": [77, 109], "encod": [79, 191, 192, 204, 205, 409], "For": [79, 85, 138, 187, 242, 256, 257, 328, 376, 391, 394, 396, 406, 409, 430, 438, 440, 441, 446, 449], "amino_acid": 79, "n": [13, 79, 115, 122, 125, 128, 132, 133, 134, 146, 149, 223, 226, 232, 235, 238, 241, 250, 253, 283, 286, 304, 328, 331, 334, 341, 390, 393, 394, 396, 398, 401, 402, 403, 404, 405, 407, 408, 412, 413, 414, 416, 417, 418, 419, 421, 425, 426, 429, 430, 431, 432, 433, 434, 438, 449, 451], "d": [79, 256, 257, 306, 322, 390, 395, 397, 407, 430, 438, 453, 455, 457], "c": [79, 132, 133, 134, 390, 393, 396, 398, 401, 402, 403, 404, 405, 407, 408, 412, 413, 414, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 429, 431, 432, 434, 438, 441, 448], "q": [79, 390, 407, 438], "h": [79, 117, 118, 119, 274, 294, 390, 398, 401, 402, 403, 405, 407, 408, 412, 413, 414, 415, 416, 417, 418, 419, 421, 424, 425, 426, 429, 431, 432, 434, 438], "k": [13, 14, 79, 115, 122, 125, 128, 137, 141, 146, 149, 220, 223, 226, 229, 232, 235, 238, 241, 250, 253, 260, 277, 278, 283, 286, 304, 318, 331, 334, 341, 390, 407, 438, 449], "m": [79, 137, 193, 256, 257, 390, 396, 397, 398, 400, 401, 402, 403, 404, 405, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 429, 430, 431, 432, 433, 434, 438, 442, 443, 445, 448, 449, 451, 453, 455, 457], "p": 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419, 420, 421, 422, 423, 424, 425, 426, 427, 429, 430, 431, 432, 433, 434, 442, 443, 445, 448, 449, 451, 453, 455, 457], "where": [81, 117, 118, 119, 205, 391, 392, 394, 396, 427, 437, 441, 442, 443, 445, 448, 449, 453, 455], "store": [81, 392, 397, 436, 441, 449], "pdb_file": 81, "remove_and_renam": 81, "ph": [81, 396, 410, 411], "remove_heteroatom": 81, "_repair_if_necessary_and_provide_path": 81, "_simulate_mut": 81, "_read_energi": 81, "_compute_sasa": 81, "solvent": [81, 158, 163, 164, 172, 176, 177, 268, 390, 394, 396, 399], "surfac": [81, 158, 163, 164, 172, 176, 177, 268, 394, 396, 399, 410], "area": [81, 158, 163, 164, 172, 176, 177, 268, 394, 396, 399, 410], "compute_st": 81, "structur": [81, 87, 88, 89, 138, 170, 392, 396, 406, 438], "compute_sasa": 81, "compute_stability_and_sasa": 81, "singl": [81, 85, 256, 257, 306, 390, 394, 397, 399, 420, 422, 423, 430, 436, 438, 444, 451], "copy_foldx_fil": 81, "copi": [81, 391, 392, 396, 437, 452], "write_mutations_to_fil": 81, 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[396, 406], "pdb": 396, "repair": [396, 410, 411], "what": [396, 437, 440, 441], "pars": 396, "wildtyp": 396, "defin": [396, 436, 437, 438], "s": [391, 396, 448], "sasa": [396, 409], "score": 396, "conclus": [396, 438, 440], "rapid": 397, "stabil": [397, 409, 411, 436], "predict": 397, "how": [397, 398, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 439, 442, 443, 444, 445, 446, 448, 449, 451, 452, 453, 454, 455, 456, 457], "warn": 397, "further": 397, "read": [397, 441], "all": [399, 406, 438], "about": [398, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 437, 442, 443, 444, 445, 446, 448, 449, 451, 452, 453, 454, 455, 456, 457], "drd3": 406, "dock": [405, 406], "tdc": [398, 401, 402, 403, 405, 406, 408, 412, 413, 414, 415, 416, 417, 418, 419, 421, 424, 425, 426, 429, 431, 432, 434], "add": 406, "binari": 406, "path": 406, "prepare_receptor": 406, "from": [], "sure": 406, "you": [406, 410, 411], "re": 406, "rfp": 409, "lambo": [409, 420], "we": [394, 409, 410, 411], "can": [409, 410, 411], "automat": 409, "solvent": 410, "access": [410, 424], "penal": 420, "logp": [420, 422], "keyword": [], "argument": [], "log": [422, 438], "solubl": 422, "quantit": 423, "estim": 423, "druglik": 423, "qed": 423, "synthet": 424, "jump": 427, "super": 427, "mario": 427, "bro": 427, "therapeut": [], "data": [], "common": [], "oracl": [], "name": 428, "see": [428, 442, 443, 444, 448, 449, 452, 455, 457], "also": 428, "continu": [394, 430], "low": 430, "intrins": 430, "dimension": 430, "white": 433, "nois": 433, "observ": [435, 438], "isol": [66, 67, 68, 69, 70, 435, 441], "process": 435, "random": [436, 454], "mroug": 436, "check": 436, "result": 436, "poli_baselin": 437, "candid": 437, "want": 438, "more": [438, 441, 442, 443, 444, 448, 449, 452, 455, 457], "complex": 438, "instanc": [69, 438], "simpl": 438, "initi": 438, "put": 438, "togeth": 438, "coupl": 438, "queri": 438, "dive": 439, "deeper": 439, "doe": 439, "under": [439, 441], "hood": [439, 441], "content": [439, 440], "usual": 440, "develop": 440, "loop": 440, "identifi": 440, "own": 440, "when": [], "have": [], "right": [], "Is": [], "factori": [], "entir": [], "need": [], "where": [], "thi": 427, "call": [], "bayesian": [442, 444, 448, 450, 451, 455, 457], "cma": 446, "es": 446, "graph": 447, "genet": 447, "latent": 450, "space": 450, "line": 451, "nsga": 452, "2": [417, 452], "templat": 456, "abstract_isolated_funct": 6, "black_box_inform": 11, "tdc_isolated_funct": 15, "foldx_isolated_funct": 27, "external_black_box": 67, "external_funct": 68, "isolated_black_box": 70, "inform": [114, 121, 124, 127, 130, 136, 139, 145, 148, 151, 160, 167, 173, 201, 213, 219, 222, 225, 228, 231, 234, 237, 240, 243, 249, 252, 255, 259, 264, 269, 272, 276, 282, 285, 288, 303, 323, 330, 333, 340, 355, 356, 391, 433], "isolated_funct": [131, 140, 152, 161, 168, 174, 202, 214, 244, 256, 273, 277, 391], "test_instancing_black_boxes_alon": 372, "specifi": 391, "__init__": 391, "happen": 441, "test_black_box_instanc": 370, "test_multi_objective_and_neg": 373, "test_core_promis": 380, "cluster": 427, "albuterol_similar": [113, 114, 115], "amlodipine_mpo": [120, 121, 122], "celecoxib_rediscoveri": [123, 124, 125], "deco_hop": [126, 127, 128], "drd2_dock": [135, 136, 137], "fexofenadine_mpo": [147, 148, 149], "gsk3_beta": [218, 219, 220], "isomer_c7h8n2o2": [221, 222, 223], "isomer_c9h10n2o2pf2cl": [224, 225, 226], "jnk3": [227, 228, 229], "median_1": [230, 231, 232], "median_2": [233, 234, 235], "mestranol_similar": [236, 237, 238], "osimetrinib_mpo": [239, 240, 241], "perindopril_mpo": [248, 249, 250], "ranolazine_mpo": [251, 252, 253], "scaffold_hop": [281, 282, 283], "sitagliptin_mpo": [284, 285, 286], "thiothixene_rediscoveri": [302, 303, 304], "troglitazone_rediscoveri": [329, 330, 331], "valsartan_smart": [332, 333, 334], "zaleplon_mpo": [339, 340, 341], "albuterol": 398, "similar": [398, 418], "drd2": 405, "gsk3\u03b2": 412, "mestranol": 418, "celecoxib": 402, "rediscoveri": [402, 429, 431], "thiothixen": 429, "troglitazon": 431, "amlodipin": 401, "mpo": [401, 408, 419, 421, 426, 434], "fexofenadin": 408, "osimetrinib": 419, "ranolazin": 421, "sitagliptin": 426, "zaleplon": 434, "deco": 403, "hop": [403, 425], "scaffold": 425, "isom": [413, 414], "c7h8n2o2": 413, "c9h10n2o2pf2cl": 414, "median": [416, 417], "1": 416, "valsartan": 432, "smart": 432, "c": 415, "jun": 415, "n": 415, "termin": 415, "kinas": 415, "3": 415, "abstract_benchmark": 2, "benchmark_inform": 10, "benchmark": [344, 345, 394], "test_benchmark_cr": 345, "hvarfner": 448, "vanilla": 448, "spars": 455, "axi": 455, "subspac": [443, 455], "saasbo": 455, "multi_observ": 71, "alebo": 442, "adapt": [442, 443], "linear": 442, "embed": 442, "trust": 457, "region": 457, "turbo": 457, "expand": 443, "baxu": 443, "bounc": 445, "probabilist": 453, "reparametr": 453, "algorithm_observer_wrapp": 40, "default_observ": 53, "ehrlich": [144, 145, 146, 407], "rmf_landscap": [271, 272, 273, 274], "conftest": 346, "toy_discrete_problem": 379, "test_dynamic_inform": 356, "lambo2": 449}, "envversion": {"sphinx.domains.c": 2, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 6, "sphinx.domains.index": 1, "sphinx.domains.javascript": 2, "sphinx.domains.math": 2, "sphinx.domains.python": 3, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx.ext.intersphinx": 1, "sphinxcontrib.bibtex": 9, "sphinx": 56}})
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References
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+ LaMBO2 — Documentation for poli and poli-baselines
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+ LaMBO2
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+ Contents
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+LaMBO2#
+
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+About#
+This is an implementation of LaMBO2 as described in [Gruver et al., 2024]. We use the official GitHub implementation provided by Genentech underneath.
+
+Note
+A tutorial on optimizing thermal stability of RFP proteins is available in poli-baselines
’ repository.
+Another minimal example is available on Colab.
+
+
+
+How to run#
+
+Warning
+This solver runs in a different conda environment than base. You will have to install cortex
to run it:
+pip install pytorch-cortex
+
+
+
+import numpy as np
+
+from poli.objective_repository import EhrlichProblemFactory
+
+from poli_baselines.solvers.bayesian_optimization.lambo2 import (
+ LaMBO2,
+)
+
+problem = EhrlichProblemFactory().create(
+ sequence_length=10,
+ motif_length=4,
+ n_motifs=2,
+ return_value_on_unfeasible=-1.0
+)
+f, x0 = problem.black_box, problem.x0
+
+solver = LaMBO2(
+ black_box=f,
+ x0=x0,
+)
+
+
+
+
+See more#
+
+By default, LaMBO2
runs with a conservative set-up for the underlying optimizer. You can find the configuration here, and you can override the set-up using the overrides
kwarg of the solver.
+
+
+
+
+References#
+If you use this solver, we expect that you cite the following resources:
+
+
+
+[1] Gruver, N., Stanton, S., Frey, N., Rudner, T. G. J., Hotzel, I., Lafrance-Vanasse, J., Rajpal, A., Cho, K., & Wilson, A. G. (2024). Protein design with guided discrete diffusion. Advances in Neural Information Processing Systems, 36.
+[2] González-Duque, M., Bartels, S., & Michael, R. (2024). poli: a libary of discrete sequence objectives [Computer software]. MachineLearningLifeScience/poli
+
+
+
+
+@article{Gruver:Lambo2:2024,
+ title={Protein design with guided discrete diffusion},
+ author={Gruver, Nate and Stanton, Samuel and Frey, Nathan and Rudner, Tim GJ and Hotzel, Isidro and Lafrance-Vanasse, Julien and Rajpal, Arvind and Cho, Kyunghyun and Wilson, Andrew G},
+ journal={Advances in Neural Information Processing Systems},
+ volume={36},
+ year={2024}
+}
+
+
+@software{Gonzalez-Duque:poli:2024,
+author = {González-Duque, Miguel and Bartels, Simon and Michael, Richard},
+month = jan,
+title = {{poli: a libary of discrete sequence objectives}},
+url = {https://github.com/MachineLearningLifeScience/poli},
+version = {0.0.1},
+year = {2024}
+}
+
+
+
+
+
+
+
+API reference#
+
+-
+class poli_baselines.solvers.bayesian_optimization.lambo2.LaMBO2(black_box: AbstractBlackBox, x0: np.ndarray | None = None, y0: np.ndarray | None = None, config_dir: Path | str | None = None, config_name: str = 'generic_training', overrides: list[str] | None = None, seed: int | None = None, max_epochs_for_retraining: int = 1)#
+LaMBO2 solver for protein-related black boxes.
+
+- Parameters
+
+black_box (AbstractBlackBox) – The black box to optimize. Must be protein-related. To ensure that the
+black box is protein-related, we verify that the alphabet inside the
+info attribute of the black box is a protein alphabet.
+x0 (np.ndarray) – The initial solutions to the black box. If not enough solutions are
+provided, the solver will generate random mutants to reach the population
+size specified in the configuration file (as cfg.num_samples).
+y0 (np.ndarray, optional) – The initial evaluations of the black box. If not provided, the solver
+will evaluate the black box on the initial solutions.
+config_dir (Path | str, optional) – The directory where the configuration files are stored. If not provided,
+the default configuration files (stored alongside this file in our
+repository) will be used. If you are interested in modifying the
+configurations, we recommend taking a look at the tutorials inside cortex.
+config_name (str, optional) – The name of the configuration file to use. Defaults to “generic_training”.
+overrides (list[str], optional) – A list of overrides to apply to the configuration file. For example,
+[“num_samples=10”, “max_epochs=5”]. To know what to override, we recommend
+taking a look at the tutorials inside cortex.
+seed (int, optional) – The random seed to use. If not provided, we use the seed provided in the
+configuration file. If provided, this seed will override the seed in the
+configuration file.
+max_epochs_for_retraining (int, optional) – The number of epochs to retrain the model after each step. Defaults to 1.
+
+
+
+
+-
+get_candidate_points_from_history() ndarray #
+Returns the current best population (whose size is specified in the
+configuration file as cfg.num_samples) from the history of the black
+box evaluations.
+
+
+
+-
+property history: dict[str, list[numpy.ndarray]]#
+Returns the history of the black box evaluations.
+
+- Returns
+The history of the black box evaluations.
+
+- Return type
+dict[str, list[np.ndarray]]
+
+
+
+
+
+-
+solve(max_iter: int = 10) None #
+Solves the black box optimization problem for a maximum of max_iter
+iterations.
+
+- Parameters
+max_iter (int, optional) – The maximum number of iterations to run the solver. Defaults to 10.
+
+
+
+
+
+-
+step() tuple[numpy.ndarray, numpy.ndarray] #
+Loads the model, runs the optimizer (LaMBO2) for the
+number of steps in the config, computes new proposal,
+evaluates on the black box and updates history.
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diff --git a/using_poli_baselines/latent_space_bo.html b/using_poli_baselines/latent_space_bo.html
index 1832fdba..a15f5d4b 100644
--- a/using_poli_baselines/latent_space_bo.html
+++ b/using_poli_baselines/latent_space_bo.html
@@ -227,6 +227,7 @@
LaMBO2
+ +Contents
+LaMBO2#
+ +About#
+This is an implementation of LaMBO2 as described in [Gruver et al., 2024]. We use the official GitHub implementation provided by Genentech underneath.
+Note
+A tutorial on optimizing thermal stability of RFP proteins is available in poli-baselines
’ repository.
Another minimal example is available on Colab.
+How to run#
+Warning
+This solver runs in a different conda environment than base. You will have to install cortex
to run it:
pip install pytorch-cortex
+
import numpy as np
+
+from poli.objective_repository import EhrlichProblemFactory
+
+from poli_baselines.solvers.bayesian_optimization.lambo2 import (
+ LaMBO2,
+)
+
+problem = EhrlichProblemFactory().create(
+ sequence_length=10,
+ motif_length=4,
+ n_motifs=2,
+ return_value_on_unfeasible=-1.0
+)
+f, x0 = problem.black_box, problem.x0
+
+solver = LaMBO2(
+ black_box=f,
+ x0=x0,
+)
+
See more#
+-
+
By default,
LaMBO2
runs with a conservative set-up for the underlying optimizer. You can find the configuration here, and you can override the set-up using theoverrides
kwarg of the solver.
+- +
References#
+If you use this solver, we expect that you cite the following resources:
+[1] Gruver, N., Stanton, S., Frey, N., Rudner, T. G. J., Hotzel, I., Lafrance-Vanasse, J., Rajpal, A., Cho, K., & Wilson, A. G. (2024). Protein design with guided discrete diffusion. Advances in Neural Information Processing Systems, 36.
+[2] González-Duque, M., Bartels, S., & Michael, R. (2024). poli: a libary of discrete sequence objectives [Computer software]. MachineLearningLifeScience/poli
+
+@article{Gruver:Lambo2:2024,
+ title={Protein design with guided discrete diffusion},
+ author={Gruver, Nate and Stanton, Samuel and Frey, Nathan and Rudner, Tim GJ and Hotzel, Isidro and Lafrance-Vanasse, Julien and Rajpal, Arvind and Cho, Kyunghyun and Wilson, Andrew G},
+ journal={Advances in Neural Information Processing Systems},
+ volume={36},
+ year={2024}
+}
+
+
+@software{Gonzalez-Duque:poli:2024,
+author = {González-Duque, Miguel and Bartels, Simon and Michael, Richard},
+month = jan,
+title = {{poli: a libary of discrete sequence objectives}},
+url = {https://github.com/MachineLearningLifeScience/poli},
+version = {0.0.1},
+year = {2024}
+}
+
+
API reference#
+-
+
- +class poli_baselines.solvers.bayesian_optimization.lambo2.LaMBO2(black_box: AbstractBlackBox, x0: np.ndarray | None = None, y0: np.ndarray | None = None, config_dir: Path | str | None = None, config_name: str = 'generic_training', overrides: list[str] | None = None, seed: int | None = None, max_epochs_for_retraining: int = 1)# +
LaMBO2 solver for protein-related black boxes.
+-
+
- Parameters +
-
+
black_box (AbstractBlackBox) – The black box to optimize. Must be protein-related. To ensure that the +black box is protein-related, we verify that the alphabet inside the +info attribute of the black box is a protein alphabet.
+x0 (np.ndarray) – The initial solutions to the black box. If not enough solutions are +provided, the solver will generate random mutants to reach the population +size specified in the configuration file (as cfg.num_samples).
+y0 (np.ndarray, optional) – The initial evaluations of the black box. If not provided, the solver +will evaluate the black box on the initial solutions.
+config_dir (Path | str, optional) – The directory where the configuration files are stored. If not provided, +the default configuration files (stored alongside this file in our +repository) will be used. If you are interested in modifying the +configurations, we recommend taking a look at the tutorials inside cortex.
+config_name (str, optional) – The name of the configuration file to use. Defaults to “generic_training”.
+overrides (list[str], optional) – A list of overrides to apply to the configuration file. For example, +[“num_samples=10”, “max_epochs=5”]. To know what to override, we recommend +taking a look at the tutorials inside cortex.
+seed (int, optional) – The random seed to use. If not provided, we use the seed provided in the +configuration file. If provided, this seed will override the seed in the +configuration file.
+max_epochs_for_retraining (int, optional) – The number of epochs to retrain the model after each step. Defaults to 1.
+
+
-
+
- +get_candidate_points_from_history() ndarray # +
Returns the current best population (whose size is specified in the +configuration file as cfg.num_samples) from the history of the black +box evaluations.
+
-
+
- +property history: dict[str, list[numpy.ndarray]]# +
Returns the history of the black box evaluations.
+-
+
- Returns +
The history of the black box evaluations.
+
+- Return type +
dict[str, list[np.ndarray]]
+
+
-
+
- +solve(max_iter: int = 10) None # +
Solves the black box optimization problem for a maximum of max_iter +iterations.
+-
+
- Parameters +
max_iter (int, optional) – The maximum number of iterations to run the solver. Defaults to 10.
+
+
-
+
- +step() tuple[numpy.ndarray, numpy.ndarray] # +
Loads the model, runs the optimizer (LaMBO2) for the +number of steps in the config, computes new proposal, +evaluates on the black box and updates history.
+