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- Title
ChemMORT: an automatic ADMET optimization platform using deep learning and multi-objective particle swarm optimization.
- Authors
Yi, Jia-Cai; Yang, Zi-Yi; Zhao, Wen-Tao; Yang, Zhi-Jiang; Zhang, Xiao-Chen; Wu, Cheng-Kun; Lu, Ai-Ping; Cao, Dong-Sheng
- Abstract
Drug discovery and development constitute a laborious and costly undertaking. The success of a drug hinges not only good efficacy but also acceptable absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties. Overall, up to 50% of drug development failures have been contributed from undesirable ADMET profiles. As a multiple parameter objective, the optimization of the ADMET properties is extremely challenging owing to the vast chemical space and limited human expert knowledge. In this study, a freely available platform called Chemical Molecular Optimization, Representation and Translation (ChemMORT) is developed for the optimization of multiple ADMET endpoints without the loss of potency (https://cadd.nscc-tj.cn/deploy/chemmort/). ChemMORT contains three modules: Simplified Molecular Input Line Entry System (SMILES) Encoder, Descriptor Decoder and Molecular Optimizer. The SMILES Encoder can generate the molecular representation with a 512-dimensional vector, and the Descriptor Decoder is able to translate the above representation to the corresponding molecular structure with high accuracy. Based on reversible molecular representation and particle swarm optimization strategy, the Molecular Optimizer can be used to effectively optimize undesirable ADMET properties without the loss of bioactivity, which essentially accomplishes the design of inverse QSAR. The constrained multi-objective optimization of the poly (ADP-ribose) polymerase-1 inhibitor is provided as the case to explore the utility of ChemMORT.
- Subjects
DEEP learning; DRUG discovery; CONSTRAINED optimization; MOLECULAR switches; MOLECULAR structure; PARTICLE swarm optimization; DRUG development
- Publication
Briefings in Bioinformatics, 2024, Vol 25, Issue 2, p1
- ISSN
1467-5463
- Publication type
Article
- DOI
10.1093/bib/bbae008