MoleGuLAR: Molecule Generation Using Reinforcement Learning with Alternating Rewards.
J Chem Inf Model
; 61(12): 5815-5826, 2021 12 27.
Article
in English
| MEDLINE | ID: covidwho-1555431
ABSTRACT
The design of new inhibitors for novel targets is a very important problem especially in the current scenario with the world being plagued by COVID-19. Conventional approaches such as high-throughput virtual screening require extensive combing through existing data sets in the hope of finding possible matches. In this study, we propose a computational strategy for de novo generation of molecules with high binding affinities to the specified target and other desirable properties for druglike molecules using reinforcement learning. A deep generative model built using a stack-augmented recurrent neural network initially trained to generate druglike molecules is optimized using reinforcement learning to start generating molecules with desirable properties like LogP, quantitative estimate of drug likeliness, topological polar surface area, and hydration free energy along with the binding affinity. For multiobjective optimization, we have devised a novel strategy in which the property being used to calculate the reward is changed periodically. In comparison to the conventional approach of taking a weighted sum of all rewards, this strategy shows an enhanced ability to generate a significantly higher number of molecules with desirable properties.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Drug Design
/
COVID-19
Limits:
Humans
Language:
English
Journal:
J Chem Inf Model
Journal subject:
Medical Informatics
/
Chemistry
Year:
2021
Document Type:
Article
Affiliation country:
Acs.jcim.1c01341
Similar
MEDLINE
...
LILACS
LIS