How to make machine learning scoring functions competitive with FEP Article Swipe
Matthew T. Warren
,
ísak Valsson
,
Charlotte M. Deane
,
Aniket Magarkar
,
Garrett M. Morris
,
Philip C. Biggin
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.26434/chemrxiv-2024-bth5z
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.26434/chemrxiv-2024-bth5z
Machine learning offers a promising approach for fast and accurate binding affin- ity predictions. However, current models often fail to generalise beyond their training data and are not robustly evaluated on a diverse range of benchmarks, limiting their application in drug discovery projects. In this work, we address these issues by intro- ducing a novel graph neural network model called AEV-PLIG (Atomic Environment Vector - Protein Ligand Interaction Graph), which encodes protein-ligand interactions via atomic environment vectors to improve generalisation. We evaluate our model on improved benchmarks, including our new out-of-distribution test set we call OOD Test, and two alternative benchmark systems used for free energy perturbation (FEP) calculations, and highlight competitive performance of AEV-PLIG across the board. Moreover, we demonstrate how augmented data can be leveraged to enhance predic- tion accuracy, and how enriching the training data with three complexes from a con- generic series of ligands binding to a target of interest improves performance further. Altogether, we show that these strategies improve the applicability of machine learn- ing scoring functions and enable state-of-the-art performance nearing the accuracy of physics-based simulation methods—but at a fraction of their computational cost. This practical approach extends the predictive capabilities of machine learning for molecular discovery, paving the way for its broader use in computer-aided drug design.
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- preprint
- Language
- en
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- https://doi.org/10.26434/chemrxiv-2024-bth5z
- https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/6675a38d5101a2ffa8274f62/original/how-to-make-machine-learning-scoring-functions-competitive-with-fep.pdf
- OA Status
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All OpenAlex metadata
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How to make machine learning scoring functions competitive with FEPWork title
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enPrimary language
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2024Year of publication
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2024-06-24Full publication date if available
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Matthew T. Warren, ísak Valsson, Charlotte M. Deane, Aniket Magarkar, Garrett M. Morris, Philip C. BigginList of authors in order
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https://doi.org/10.26434/chemrxiv-2024-bth5zPublisher landing page
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https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/6675a38d5101a2ffa8274f62/original/how-to-make-machine-learning-scoring-functions-competitive-with-fep.pdfDirect link to full text PDF
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goldOpen access status per OpenAlex
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https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/6675a38d5101a2ffa8274f62/original/how-to-make-machine-learning-scoring-functions-competitive-with-fep.pdfDirect OA link when available
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2025: 3, 2024: 1Per-year citation counts (last 5 years)
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| abstract_inverted_index.broader | 208 |
| abstract_inverted_index.current | 15 |
| abstract_inverted_index.design. | 213 |
| abstract_inverted_index.diverse | 32 |
| abstract_inverted_index.encodes | 70 |
| abstract_inverted_index.enhance | 128 |
| abstract_inverted_index.extends | 193 |
| abstract_inverted_index.generic | 144 |
| abstract_inverted_index.improve | 78, 163 |
| abstract_inverted_index.ligands | 147 |
| abstract_inverted_index.machine | 167, 198 |
| abstract_inverted_index.nearing | 176 |
| abstract_inverted_index.network | 57 |
| abstract_inverted_index.predic- | 129 |
| abstract_inverted_index.scoring | 170 |
| abstract_inverted_index.systems | 101 |
| abstract_inverted_index.vectors | 76 |
| abstract_inverted_index.AEV-PLIG | 60, 114 |
| abstract_inverted_index.However, | 14 |
| abstract_inverted_index.accuracy | 178 |
| abstract_inverted_index.accurate | 9 |
| abstract_inverted_index.approach | 5, 192 |
| abstract_inverted_index.evaluate | 81 |
| abstract_inverted_index.fraction | 185 |
| abstract_inverted_index.further. | 156 |
| abstract_inverted_index.improved | 85 |
| abstract_inverted_index.improves | 154 |
| abstract_inverted_index.interest | 153 |
| abstract_inverted_index.learning | 1, 199 |
| abstract_inverted_index.limiting | 36 |
| abstract_inverted_index.robustly | 28 |
| abstract_inverted_index.training | 23, 136 |
| abstract_inverted_index.Moreover, | 118 |
| abstract_inverted_index.accuracy, | 131 |
| abstract_inverted_index.augmented | 122 |
| abstract_inverted_index.benchmark | 100 |
| abstract_inverted_index.complexes | 140 |
| abstract_inverted_index.discovery | 41 |
| abstract_inverted_index.enriching | 134 |
| abstract_inverted_index.evaluated | 29 |
| abstract_inverted_index.functions | 171 |
| abstract_inverted_index.highlight | 110 |
| abstract_inverted_index.including | 87 |
| abstract_inverted_index.leveraged | 126 |
| abstract_inverted_index.molecular | 201 |
| abstract_inverted_index.practical | 191 |
| abstract_inverted_index.projects. | 42 |
| abstract_inverted_index.promising | 4 |
| abstract_inverted_index.discovery, | 202 |
| abstract_inverted_index.generalise | 20 |
| abstract_inverted_index.predictive | 195 |
| abstract_inverted_index.simulation | 181 |
| abstract_inverted_index.strategies | 162 |
| abstract_inverted_index.Altogether, | 157 |
| abstract_inverted_index.Environment | 62 |
| abstract_inverted_index.Interaction | 67 |
| abstract_inverted_index.alternative | 99 |
| abstract_inverted_index.application | 38 |
| abstract_inverted_index.benchmarks, | 35, 86 |
| abstract_inverted_index.competitive | 111 |
| abstract_inverted_index.demonstrate | 120 |
| abstract_inverted_index.environment | 75 |
| abstract_inverted_index.performance | 112, 155, 175 |
| abstract_inverted_index.capabilities | 196 |
| abstract_inverted_index.interactions | 72 |
| abstract_inverted_index.perturbation | 106 |
| abstract_inverted_index.predictions. | 13 |
| abstract_inverted_index.applicability | 165 |
| abstract_inverted_index.calculations, | 108 |
| abstract_inverted_index.computational | 188 |
| abstract_inverted_index.methods—but | 182 |
| abstract_inverted_index.physics-based | 180 |
| abstract_inverted_index.computer-aided | 211 |
| abstract_inverted_index.protein-ligand | 71 |
| abstract_inverted_index.generalisation. | 79 |
| abstract_inverted_index.state-of-the-art | 174 |
| abstract_inverted_index.out-of-distribution | 90 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.84827911 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |