Search Results for author:
Found 4 papers, 0 papers with code
CHA2: CHemistry Aware Convex Hull Autoencoder Towards Inverse Molecular Design
The continuous property of the latent space, which characterizes the discrete chemical structures, provides a flexible representation for inverse design in order to discover novel molecules.
Machine learning for the prediction of safe and biologically active organophosphorus molecules
Drug discovery is a complex process with a large molecular space to be considered.
Generative Enriched Sequential Learning (ESL) Approach for Molecular Design via Augmented Domain Knowledge
Deploying generative machine learning techniques to generate novel chemical structures based on molecular fingerprint representation has been well established in molecular design.
Deep Evolutionary Learning for Molecular Design
In this paper, we propose a deep evolutionary learning (DEL) process that integrates fragment-based deep generative model and multi-objective evolutionary computation for molecular design.
