强化学习
计算机科学
对偶(语法数字)
交叉口(航空)
人工智能
生物信息学
发电机(电路理论)
相似性(几何)
生成语法
机器学习
计算生物学
化学
基因
生物
工程类
图像(数学)
物理
文学类
艺术
航空航天工程
功率(物理)
量子力学
生物化学
作者
Fengqing Lu,Mufei Li,Xiaoping Min,Chunyan Li,Xiangxiang Zeng
摘要
Artificial intelligence, such as deep generative methods, represents a promising solution to de novo design of molecules with the desired properties. However, generating new molecules with biological activities toward two specific targets remains an extremely difficult challenge. In this work, we conceive a novel computational framework, herein called dual-target ligand generative network (DLGN), for the de novo generation of bioactive molecules toward two given objectives. Via adversarial training and reinforcement learning, DLGN treats a sequence-based simplified molecular input line entry system (SMILES) generator as a stochastic policy for exploring chemical spaces. Two discriminators are then used to encourage the generation of molecules that belong to the intersection of two bioactive-compound distributions. In a case study, we employ our methods to design a library of dual-target ligands targeting dopamine receptor D2 and 5-hydroxytryptamine receptor 1A as new antipsychotics. Experimental results demonstrate that the proposed model can generate novel compounds with high similarity to both bioactive datasets in several structure-based metrics. Our model exhibits a performance comparable to that of various state-of-the-art multi-objective molecule generation models. We envision that this framework will become a generally applicable approach for designing dual-target drugs in silico.
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