Selective Inhibitor Design for Kinase Homologs Using Multiobjective Monte Carlo Tree Search.
J Chem Inf Model
; 62(22): 5351-5360, 2022 11 28.
Article
de En
| MEDLINE
| ID: mdl-36334094
ABSTRACT
Designing highly selective molecules for a drug target protein is a challenging task in drug discovery. This task can be regarded as a multiobjective problem that simultaneously satisfies criteria for various objectives, such as selectivity for a target protein, pharmacokinetic endpoints, and drug-like indices. Recent breakthroughs in artificial intelligence have accelerated the development of molecular structure generation methods, and various researchers have applied them to computational drug designs and successfully proposed promising drug candidates. However, designing efficient selective inhibitors with releasing activities against various homologs of a target protein remains a difficult issue. In this study, we developed a de novo structure generator based on reinforcement learning that is capable of simultaneously optimizing multiobjective problems. Our structure generator successfully proposed selective inhibitors for tyrosine kinases while optimizing 18 objectives consisting of inhibitory activities against 9 tyrosine kinases, 3 pharmacokinetics endpoints, and 6 other important properties. These results show that our structure generator and optimization strategy for selective inhibitors will contribute to the further development of practical structure generators for drug designs.
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Intelligence artificielle
/
Méthode de Monte Carlo
Type d'étude:
Health_economic_evaluation
/
Prognostic_studies
Langue:
En
Journal:
J Chem Inf Model
Sujet du journal:
INFORMATICA MEDICA
/
QUIMICA
Année:
2022
Type de document:
Article
Pays d'affiliation:
Japon