A Deep Generative Model for Molecule Optimization via One Fragment Modification.
A Deep Generative Model for Molecule Optimization via One Fragment Modification.
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DOI：
10.1038/s42256-021-00410-2
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发表时间：
2021-12
影响因子：
23.8
通讯作者：
Ning X
中科院分区：
文献类型：
--
作者：
Chen Z;Min MR;Parthasarathy S;Ning X
分子优化是药物开发中的关键步骤，通过化学修饰来改善候选药物的期望性质。我们在分子图上开发了一种新的用于分子优化的深度生成模型Modof。Modof通过预测分子上的单个断开位点以及在该位点去除和/或添加片段来修饰给定的分子。将多个相同的Modof模型的管道实现到Modof管道中，以在多个断开位点处修改输入分子。在这里，我们表明，Modof-pipe能够保留主要的分子支架，允许控制中间优化步骤，并更好地约束分子相似性。Modof-pipe在基准数据集上的表现优于最先进的方法：在没有分子相似性约束的情况下，Modof-pipe在合成可及性和环尺寸的惩罚下，辛醇-水分配系数提高了81.2%;如果优化的分子与优化前的分子至少相似0.2，0.4和0.6，则分别提高了51.2%，25.6%和9.2%。Modof-pipe进一步增强为Modof-pipem，允许将一个分子修饰为多个优化分子。Modof-pipem实现了额外的性能改进，比Modof-pipe至少好17.8%。
Molecule optimization is a critical step in drug development to improve desired properties of drug candidates through chemical modification. We developed a novel deep generative model Modof over molecular graphs for molecule optimization. Modof modifies a given molecule through the prediction of a single site of disconnection at the molecule and the removal and/or addition of fragments at that site. A pipeline of multiple, identical Modof models is implemented into Modof-pipe to modify an input molecule at multiple disconnection sites. Here we show that Modof-pipe is able to retain major molecular scaffolds, allow controls over intermediate optimization steps and better constrain molecule similarities. Modof-pipe outperforms the state-of-the-art methods on benchmark datasets: without molecular similarity constraints, Modof-pipe achieves 81.2% improvement in octanol-water partition coefficient penalized by synthetic accessibility and ring size; and 51.2%, 25.6% and 9.2% improvement if the optimized molecules are at least 0.2, 0.4 and 0.6 similar to those before optimization, respectively. Modof-pipe is further enhanced into Modof-pipem to allow modifying one molecule to multiple optimized ones. Modof-pipem achieves additional performance improvement as at least 17.8% better than Modof-pipe.
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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：
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：
21.8
作者：
Murray, Christopher W.;Rees, David C.
通讯作者：
Rees, David C.
影响因子：
5.6
作者：
Liu, Junfeng;Ning, Xia
通讯作者：
Ning, Xia