NeuralDock: Rapid and Conformation-Agnostic Docking of Small Molecules.
NeuralDock: Rapid and Conformation-Agnostic Docking of Small Molecules.
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DOI：
10.3389/fmolb.2022.867241
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发表时间：
2022
影响因子：
5
通讯作者：
Dokholyan NV
中科院分区：
文献类型：
--
作者：
Sha CM;Wang J;Dokholyan NV
在药物发现过程中，虚拟筛选是传统高通量筛选的一种具有成本和时间效益的替代方法。基于结构的分子对接和基于配体的化学信息学这两种虚拟筛选方法都存在计算成本、低准确性和/或依赖于与给定靶标结合的配体的先验知识的问题。在这里，我们提出了一个神经网络框架NeuralDock，它将高质量计算对接的过程加速了106倍，并且不需要与给定目标结合的配体的先验知识。通过近似蛋白质-小分子构象采样和基于能量的评分，NeuralDock基于蛋白质口袋3D结构和小分子拓扑结构准确预测蛋白质-小分子对的结合能和亲和力。我们使用NeuralDock和25个GPU在21小时内将来自ZINC数据库的9.37亿个分子与超氧化物歧化酶-1对接，我们使用MedusaDock进行了物理对接验证。由于它的速度和准确性，NeuralDock可能在大规模化学库的强力虚拟筛选和生成药物模型的训练中非常有用。
Virtual screening is a cost- and time-effective alternative to traditional high-throughput screening in the drug discovery process. Both virtual screening approaches, structure-based molecular docking and ligand-based cheminformatics, suffer from computational cost, low accuracy, and/or reliance on prior knowledge of a ligand that binds to a given target. Here, we propose a neural network framework, NeuralDock, which accelerates the process of high-quality computational docking by a factor of 106, and does not require prior knowledge of a ligand that binds to a given target. By approximating both protein-small molecule conformational sampling and energy-based scoring, NeuralDock accurately predicts the binding energy, and affinity of a protein-small molecule pair, based on protein pocket 3D structure and small molecule topology. We use NeuralDock and 25 GPUs to dock 937 million molecules from the ZINC database against superoxide dismutase-1 in 21 h, which we validate with physical docking using MedusaDock. Due to its speed and accuracy, NeuralDock may be useful in brute-force virtual screening of massive chemical libraries and training of generative drug models.
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影响因子：
5.6
作者：
Francoeur PG;Masuda T;Sunseri J;Jia A;Iovanisci RB;Snyder I;Koes DR
通讯作者：
Koes DR
影响因子：
64.8
作者：
Jumper J;Evans R;Pritzel A;Green T;Figurnov M;Ronneberger O;Tunyasuvunakool K;Bates R;Žídek A;Potapenko A;Bridgland A;Meyer C;Kohl SAA;Ballard AJ;Cowie A;Romera-Paredes B;Nikolov S;Jain R;Adler J;Back T;Petersen S;Reiman D;Clancy E;Zielinski M;Steinegger M;Pacholska M;Berghammer T;Bodenstein S;Silver D;Vinyals O;Senior AW;Kavukcuoglu K;Kohli P;Hassabis D
通讯作者：
Hassabis D
影响因子：
21.8
作者：
通讯作者：
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影响因子：
14.8
作者：
Forli S;Huey R;Pique ME;Sanner MF;Goodsell DS;Olson AJ
通讯作者：
Olson AJ
影响因子：
3.3
作者：
Fan, Mengran;Wang, Jian;Jiang, Huaipan;Feng, Yilin;Mahdavi, Mehrdad;Madduri, Kamesh;Kandemir, Mahmut T.;Dokholyan, Nikolay, V
通讯作者：
Dokholyan, Nikolay, V