AIMC Topic: Drug Discovery

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Deep Learning vs Classical Methods in Potency and ADME Prediction: Insights from a Computational Blind Challenge.

Journal of chemical information and modeling
Reliable prediction of compound potency and the ADME profile is crucial in drug discovery. With the recent surge of AI and deep learning frameworks, it remains unclear whether these modern techniques offer statistically significant improvement over t...

Recent strategies and methodological advances for microbial natural product research in the post-genomics era.

Archives of microbiology
Microorganisms remain a prolific source of bioactive compounds, yet discovery efforts are often hindered by traditional methods and the repeated isolation of known molecules. In the post-genomics era, advances in genome mining and multi-omics technol...

Active learning framework leveraging transcriptomics identifies modulators of disease phenotypes.

Science (New York, N.Y.)
Phenotypic drug screening remains constrained by the vastness of chemical space and the technical challenges of scaling experimental workflows. To overcome these barriers, computational methods have been developed to prioritize compounds, but they re...

An interpretable geometric graph neural network for enhancing the generalizability of drug-target interaction prediction.

BMC biology
BACKGROUND: Accurate prediction of drug-target interactions (DTIs) is essential for advancing drug discovery. Although numerous computational methods have been proposed, many exhibit limited generalization, particularly when dealing with unseen drugs...

MoleculeFormer is a GCN-transformer architecture for molecular property prediction.

Communications biology
Artificial intelligence is increasingly important in drug discovery, particularly in molecular property prediction. Graph Neural Networks can model molecular structures as graphs, using structural data to predict molecular properties and biological a...

Benchmarking Sequence-Based Compound-Protein Interaction Prediction through Constructing a Debiased Data Set CDPN.

Journal of chemical information and modeling
Accurate prediction of compound-protein interactions (CPIs) is critical for drug discovery, but existing data sets often suffer from biases that hinder model generalization. Here, we first highlighted that over-represented molecular scaffolds and imb...

Machine learning-powered discovery of a novel berberine derivative inducing SCD-dependent ferroptosis in osteosarcoma.

Journal of translational medicine
BACKGROUND: Despite decades of therapeutic development, osteosarcoma survival remains poor. Although berberine (BBR) shows anti-tumor activity, its efficacy is limited. We addressed this through structural modification and machine learning-guided dis...

AI-driven discovery of dual antiaging and anti-AD therapeutics via PROTAC target deconvolution of a super-enhancer-regulated axis.

Science advances
The lack of safe, durable therapeutics that act against both biological aging and Alzheimer's disease is an unmet clinical need. To bridge this gap, we devised an artificial intelligence (AI)-enabled approach that pairs rapid compound triage with mec...

CANDID-CNS: AI Unlocks Stereochemistry and Beyond Rule of 5 to Predict CNS Penetration of Small Molecules.

Journal of chemical information and modeling
Neuroscience is the most difficult therapeutic area for pharmaceutical drug discovery. The blood-brain barrier (BBB) prevents ∼100% of large molecules and >98% of small molecules from penetrating the central nervous system (CNS). Most small molecule ...

Generative AI for the Design of Molecules: Advances and Challenges.

Journal of chemical information and modeling
The design of novel molecules underpins advances in both drug discovery and biomaterials engineering. Traditional approaches, from natural product isolation to high-throughput screening, have delivered important therapeutics but remain costly, ineffi...