Jinwen Shan and Changge Ji* Pages 460 - 466 ( 7 )
Background: Bioisosteric replacement is widely used in drug design for lead optimization. However, the identification of a suitable bioisosteric group is not an easy task.
Methods: In this work, we present MolOpt, a web server for in silico drug design using bioisosteric transformation. Potential bioisosteric transformation rules were derived from data mining, deep generative machine learning and similarity comparison. MolOpt tries to assist the medicinal chemist in his/her search for what to make next.
Results and Discussion: By replacing molecular substructures with similar chemical groups, MolOpt automatically generates lists of analogues. MolOpt also evaluates forty important pharmacokinetic and toxic properties for each newly designed molecule. The transformed analogues can be assessed for possible future study.
Conclusion: MolOpt is useful for the identification of suitable lead optimization ideas. The MolOpt Server is freely available for use on the web at http://xundrug.cn/molopt.
Bioisosteric replacement, lead optimization, in silico drug design, data mining, machine learning, web server.
NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai, 200062, NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai, 200062