Multi-constraint molecular generation based on conditional transformer, knowledge distillation and reinforcement learning
Abstract
Machine learning-based generative models can generate novel molecules with desirable physiochemical and pharmacological properties from scratch. Many excellent generative models have been proposed, but multi-objective optimizations in molecular generative tasks are still quite challenging for most existing models. Here we proposed the multi-constraint molecular generation (MCMG) approach that can satisfy multiple constraints by combining...
Paper Details
Title
Multi-constraint molecular generation based on conditional transformer, knowledge distillation and reinforcement learning
Published Date
Oct 18, 2021
Journal
Volume
3
Issue
10
Pages
914 - 922
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