Fang Wan

The paper uses a genomic language model to cluster and annotate noncoding sequences in prokaryotic genomes, identifying structured RNAs and their potential functions.

The paper identifies antimicrobial peptides encoded in ancient human mitochondrial genomes and validates their activity against bacteria.

This paper introduces a conditional variational autoencoder for antimicrobial peptide generation, combining QSAR data and cross-entropy optimization to overcome limitations in generative models.

This paper introduces APEXGO, a generative AI framework for optimizing antimicrobial peptides, achieving high success rates in enhancing antimicrobial activity and outperforming existing methods.

Kinome-AI predicts activating kinase mutations using machine learning, achieving high accuracy without requiring structural inputs for new mutations.

This study introduces AMP-Diffusion, a generative model for designing antimicrobial peptides, which produces candidates with broad-spectrum antibacterial activity and low toxicity.

This study uses deep learning to analyze archaeal proteomes, identifying 12,623 potential antimicrobial molecules, with 93% showing in vitro activity and one candidate effective in vivo against bacterial infections.

The paper improves functional protein generation by optimizing likelihood in both amino acid sequence and latent space, outperforming existing models in AMP and MDH generation.

This paper introduces ApexDuo, a deep learning model that generates peptides with both cell-penetrating and antimicrobial properties, and demonstrates its effectiveness against intracellular pathogens.

A uniformly processed set of 2,440,377 bacterial and archaeal genomes with search tools and annotations, plus an AI-mined peptide set yielding ATB20, active in a mouse skin infection model.

This paper presents a data-driven drug repositioning framework that identifies CVL218, a PARP1 inhibitor, as a potential therapeutic for COVID-19 through in silico and experimental validation.

MoTSE estimates task similarity for molecular property prediction to improve transfer learning performance by leveraging graph neural networks and interpretability.

CAMP predicts peptide-protein interactions and binding residues using a convolutional attention-based neural network, outperforming existing methods on benchmark datasets.

MONN predicts non-covalent interactions and binding affinities between compounds and proteins, outperforming existing methods and providing interpretable insights.

ACME improves peptide-MHC class I binding prediction using attention-based deep learning, outperforming existing methods and providing interpretable insights.

NeoDTI predicts drug-target interactions by integrating heterogeneous network data, showing improved performance and novel predictions supported by prior studies.

Compound and protein embeddings learned from unlabelled data feed a deep network for compound-protein interaction prediction, reported to perform better on ChEMBL, BindingDB and DrugBank.