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.