Medicinal chemistry & drug discovery
161 studies18 in 7 days47 in 30 days
Filters
Latest
161 studies
- Network-driven discovery of repurposable drugs targeting hallmarks of aging
- From the Gut to the Brain: Modeling Intestinal Absorption and Assessing Blood–Brain-Barrier Permeability of Small-Molecule Drugs by IAM Chromatography
- URSA: Chemistry-Aware Benchmark for Utilitarian Retrosynthesis Assessment
- Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements
- INTEGRATIVE PHARMACOGENOMIC ANALYSIS OF DRUG RESPONSE HETEROGENEITY ACROSS CANCER CELL LINES: INSIGHTS FROM LARGE-SCALE GDSC DATA
- Deep learning–enabled discovery of antibiotics effective against Neisseria gonorrhoeae
- Integrating machine learning-based molecular design with experimental validation for the discovery of EGFR inhibitors in lung cancer
- Back to basics: Observed statistics are sufficient to predict drug responses
- Atomic-level protein–ligand recognition with PBCNet2.0 for probe discovery
- Geometry-Aware Line Graph Transformer Pretraining for Molecular Property Prediction
- AI in Drug Discovery: Clinical Failures, Regulatory Reality, and the Validation Crisis Behind the Hype
- Pushing the Limits of One-Dimensional NMR Spectroscopy for Automated Structure Elucidation Using Artificial Intelligence
- Natural Products as Promising Pharmacological Agents Against Cancer: A Holistic Overview of Their Anti-Cancer Mechanisms of Action of the Last Five Years
- In silico pharmacological analysis of Tinospora cordifolia compounds targeting African swine fever virus B175L
- From Answers to States: Verifiable Process-Level Evaluation of Chemical Reasoning in Large Language Models
- Multi-omics and artificial intelligence for precision drug discovery and potential clinical applications
- ACAP-AG: A graph neural network model for anticancer activity prediction by integrating heterogeneous data.
- Reproducibility, validation, and failure modes across classical and AI-driven molecular docking
- Weighted Molecular Views with Global Attention Tokens
- Standardized numbering and alignment of the KPC family of β-lactamases
- Predicting Blood–Brain Barrier Permeability from Experimental Data: An Interpretable and Externally Validated Machine Learning Framework
- FLOWR: flow matching for structure-aware de novo, interaction- and fragment-based ligand generation
- GastroDL-Fusion: A Dual-Modal Deep Learning Framework Integrating Protein–Ligand Complexes and Gene Sequences for Gastrointestinal Disease Drug Discovery
- Mapping the avoid-ome: a systematic open-science approach to predictive ADMET
- Progress in Natural Products Target Discovery Technology