1
|
Xia X, Liu Y, Zheng C, Zhang X, Wu Q, Gao X, Zeng X, Su Y. Evolutionary Multiobjective Molecule Optimization in an Implicit Chemical Space. J Chem Inf Model 2024. [PMID: 38870455 DOI: 10.1021/acs.jcim.4c00031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2024]
Abstract
Optimization techniques play a pivotal role in advancing drug development, serving as the foundation of numerous generative methods tailored to efficiently design optimized molecules derived from existing lead compounds. However, existing methods often encounter difficulties in generating diverse, novel, and high-property molecules that simultaneously optimize multiple drug properties. To overcome this bottleneck, we propose a multiobjective molecule optimization framework (MOMO). MOMO employs a specially designed Pareto-based multiproperty evaluation strategy at the molecular sequence level to guide the evolutionary search in an implicit chemical space. A comparative analysis of MOMO with five state-of-the-art methods across two benchmark multiproperty molecule optimization tasks reveals that MOMO markedly outperforms them in terms of diversity, novelty, and optimized properties. The practical applicability of MOMO in drug discovery has also been validated on four challenging tasks in the real-world discovery problem. These results suggest that MOMO can provide a useful tool to facilitate molecule optimization problems with multiple properties.
Collapse
Affiliation(s)
- Xin Xia
- The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University, Hefei 230601, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, 5089 Wangjiang West Road, Hefei 230088, AnhuiChina
| | - Yiping Liu
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410012, China
| | - Chunhou Zheng
- The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University, Hefei 230601, China
| | - Xingyi Zhang
- The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University, Hefei 230601, China
| | - Qingwen Wu
- The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University, Hefei 230601, China
| | - Xin Gao
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Xiangxiang Zeng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410012, China
| | - Yansen Su
- The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University, Hefei 230601, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, 5089 Wangjiang West Road, Hefei 230088, AnhuiChina
| |
Collapse
|
2
|
Chen Z, Min MR, Parthasarathy S, Ning X. A deep generative model for molecule optimization via one fragment modification. NAT MACH INTELL 2021; 3:1040-1049. [DOI: 10.1038/s42256-021-00410-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
|
3
|
Deep Learning Applied to Ligand-Based De Novo Drug Design. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2021; 2390:273-299. [PMID: 34731474 DOI: 10.1007/978-1-0716-1787-8_12] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
In the latest years, the application of deep generative models to suggest virtual compounds is becoming a new and powerful tool in drug discovery projects. The idea behind this review is to offer an updated view on de novo design approaches based on artificial intelligent (AI) algorithms, with a particular focus on ligand-based methods. We start this review by reporting a brief overview of the most relevant de novo design approaches developed before the use of AI techniques. We then describe the nowadays most common neural network architectures employed in ligand-based de novo design, together with an up-to-date list of more than 100 deep generative models found in the literature (2017-2020). In order to show how deep generative approaches are applied into drug discovery context, we report all the now available studies in which generated compounds have been synthetized and their biological activity tested. Finally, we discuss what we envisage as beneficial future directions for further application of deep generative models in de novo drug design.
Collapse
|