Area of research
Computational Theory and Mathematics · Materials Chemistry
Research interest
Research focused on Chemical space and Autoencoder, with related work in Mondrian, Delirium, Variety (cybernetics). Notable publications include 'REINVENT 2.0: An AI Tool for De Novo Drug Design', 'A de novo molecular generation method using latent vector based generative adversarial network', and 'Randomized SMILES strings improve the quality of molecular generative models'.
Artificial intelligence in drug development for delirium and Alzheimer’s disease
PharmaBench: Enhancing ADMET benchmarks with large language models
REINVENT 2.0: An AI Tool for De Novo Drug Design
A de novo molecular generation method using latent vector based generative adversarial network
Randomized SMILES strings improve the quality of molecular generative models
Applying Mondrian Cross-Conformal Prediction To Estimate Prediction Confidence on Large Imbalanced Bioactivity Data Sets