AIMC Topic: Drug Discovery

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AI-driven peptide discovery for endometrial cancer: deep generative modeling and molecular simulation in the big data era.

Journal of computer-aided molecular design
The integration of artificial intelligence (AI) with molecular modeling offers new opportunities to accelerate therapeutic discovery. In this study, we developed an AI-driven generative pipeline combining deep reinforcement learning (DRL), generative...

Prodrug-ML: prodrug-likeness prediction via machine learning on sampled negative decoys.

Journal of computer-aided molecular design
A prodrug is a pharmacologically inactive (or attenuated) derivative that undergoes bioreversible transformation in vivo to release an active parent drug, enabling temporary optimization of properties such as solubility, permeability, and targeting. ...

Deep contrastive learning enables genome-wide virtual screening.

Science (New York, N.Y.)
Recent breakthroughs in protein structure prediction have opened new avenues for genome-wide drug discovery, yet existing virtual screening methods remain computationally prohibitive. We present DrugCLIP, a contrastive learning framework that achieve...

Leveraging AI for cell biology discovery.

Biochemical Society transactions
Artificial intelligence (AI) has become a transformative tool in cell biology, driving discoveries through the analysis of complex biological data. This review explores the diverse applications of AI, including its impact on microscopy, imaging, drug...

Deep learning-assisted discovery of a potent and cell-active inhibitor of RNA N-methyladenosine recognition protein YTHDC2.

Nature communications
YTHDC2, a unique YTH-domain-containing protein that recognizes N6-methyladenosine (mA) on RNA, plays critical roles in diverse pathological processes and represents a promising therapeutic target. Despite its potential, no potent small-molecule inhib...

A Multimodal Drug-Target Affinity Prediction Framework with Pretrained Models and Hierarchical Graph Transformer.

Journal of chemical information and modeling
Drug-target affinity (DTA) prediction is crucial in drug discovery. It enables researchers to elucidate the complex interaction mechanisms between candidate drugs and biological targets. However, current methods have limitations in capturing global s...

AI-driven molecular modeling and design: from property prediction to drug generation.

Journal of computer-aided molecular design
Integrating the techniques of deep learning, particularly graph neural network models, has made a significant advancement in drug discovery by facilitating effective exploration of chemical spaces and precise prediction of molecular properties. This ...

Structure-Aware Heterogeneous Information Fusion Framework for Protein-Ligand Binding Affinity Prediction.

Journal of chemical information and modeling
Accurate prediction of protein-ligand binding affinities (PLAs) is essential for drug discovery and development. Recent advancements suggest that transforming protein-ligand complexes into heterogeneous graph representations may offer a viable soluti...

AI-powered IC50 prediction for p53 inhibitors drug-target interaction via hybrid graph neural networks.

Journal of computer-aided molecular design
In recent decades, the rapid pace of digital transformation marks a transformative era for the healthcare and pharmaceutical industries. The incorporation of innovative technology, specifically Artificial Intelligence (AI) and its derivatives, has dr...

Mass Spectrometry Proteomics: A Key to Faster Drug Discovery.

Journal of medicinal chemistry
Mass spectrometry (MS)-based proteomics is a disruptive platform in drug discovery that offers an exhaustive view of the proteome's complexity. Focusing on bottom-up MS proteomics, this technology enables high-throughput analysis of protein expressio...