DeepFrag: a deep convolutional neural network for fragment-based lead optimization.
DeepFrag: a deep convolutional neural network for fragment-based lead optimization.
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DeepFrag：一个深度卷积神经网络，用于基于片段的铅优化。
DOI：
10.1039/d1sc00163a
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发表时间：
2021-05-08
期刊：
影响因子：
8.4
通讯作者：
Durrant JD
中科院分区：
文献类型：
--
作者：
Green H;Koes DR;Durrant JD
近年来，机器学习越来越多地应用于计算机辅助药物发现领域，在结合亲和力预测，虚拟筛选和QSAR方面取得了显着进展。令人惊讶的是，它很少被应用于铅优化，识别可能添加到已知配体以提高其结合亲和力的化学片段的过程。我们在这里描述了一个深度卷积神经网络，它可以根据受体/配体复合物的结构预测适当的片段。在已知配体与缺失（删除）片段的独立基准中，我们的DeepFrag模型从超过6500的集合中选择了已知（正确）片段，约58%的时间。即使没有选择已知/正确的片段，顶部片段通常在化学上相似，并且可以很好地代表有效的取代。我们在Apache许可证2.0版的条款下发布经过训练的DeepFrag模型和相关软件。DeepFrag是一种机器学习模型，旨在帮助进行潜在客户优化。它建议适当的片段添加给定的蛋白质受体和结合的小分子配体的3D结构。
Machine learning has been increasingly applied to the field of computer-aided drug discovery in recent years, leading to notable advances in binding-affinity prediction, virtual screening, and QSAR. Surprisingly, it is less often applied to lead optimization, the process of identifying chemical fragments that might be added to a known ligand to improve its binding affinity. We here describe a deep convolutional neural network that predicts appropriate fragments given the structure of a receptor/ligand complex. In an independent benchmark of known ligands with missing (deleted) fragments, our DeepFrag model selected the known (correct) fragment from a set over 6500 about 58% of the time. Even when the known/correct fragment was not selected, the top fragment was often chemically similar and may well represent a valid substitution. We release our trained DeepFrag model and associated software under the terms of the Apache License, Version 2.0. DeepFrag is a machine-learning model designed to assist with lead optimization. It recommends appropriate fragment additions given the 3D structures of a protein receptor and bound small-molecule ligand.
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影响因子：
18.2
作者：
Gómez-Bombarelli R;Wei JN;Duvenaud D;Hernández-Lobato JM;Sánchez-Lengeling B;Sheberla D;Aguilera-Iparraguirre J;Hirzel TD;Adams RP;Aspuru-Guzik A
通讯作者：
Aspuru-Guzik A
影响因子：
16.6
作者：
Douangamath A;Fearon D;Gehrtz P;Krojer T;Lukacik P;Owen CD;Resnick E;Strain-Damerell C;Aimon A;Ábrányi-Balogh P;Brandão-Neto J;Carbery A;Davison G;Dias A;Downes TD;Dunnett L;Fairhead M;Firth JD;Jones SP;Keeley A;Keserü GM;Klein HF;Martin MP;Noble MEM;O'Brien P;Powell A;Reddi RN;Skyner R;Snee M;Waring MJ;Wild C;London N;von Delft F;Walsh MA
通讯作者：
Walsh MA
影响因子：
5.6
作者：
Pereira, Janaina Cruz;Caffarena, Ernesto Raul;dos Santos, Cicero Nogueira
通讯作者：
dos Santos, Cicero Nogueira
影响因子：
5.4
作者：
Durrant JD;McCammon JA
通讯作者：
McCammon JA
影响因子：
5.8
作者：
Jimenez, J.;Doerr, S.;De Fabritiis, G.
通讯作者：
De Fabritiis, G.