AI in drug discovery has shown limited clinically relevant impact despite decades of development, highlighting the need for improved translational focus.
- 7 cites
Computational Drug Discovery Methods
Moving in Bioinformatics, Nature Reviews Drug Discovery.
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PCIM-DTA achieves state-of-the-art performance in drug-target affinity prediction under various cold-start scenarios, including cold-drug and cold-target conditions.
- 2 cites
Latest in Computational Drug Discovery Methods
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A two-step machine learning approach enables transferable prediction of small-molecule retention times across different chromatographic conditions, outperforming existing methods.
- Open access
The Virtual Biotech, a multi-agent AI system, improves drug development success rates by 48% and reduces adverse events by 32% through integrated analysis of biological data.
- 1 cites
11.3% of randomly modified ligands across six targets improved potency tenfold or more, revealing a surprising baseline for ligand optimization success.
- Open access
Spurious model comparisons are prevalent in biomedical AI, with nearly two-thirds of studies using invalid tests that inflate false-positive rates.
- Open access
- 1 cites
TDiMS descriptor outperforms existing methods by accurately capturing nonlocal intramolecular interactions with 20% higher predictive accuracy in property prediction tasks.
- Open access
Machine learning identifies key chemical features, including nitrogen-containing aromatic scaffolds, that enhance mycobacterial outer membrane permeation, improving antibiotic potential.
- Open access
Benchmarking foundation models in biomedicine reveals significant challenges in testing their limitations and evaluating their utility.
- 1 cites
Gen-COMPAS enables efficient sampling of biomolecular transition pathways within nanoseconds to microseconds, surpassing traditional methods by reconstructing key states without predefined variables.
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