Computational Drug Discovery Methods

Computational Repurposing of FDA Approved Drugs Against Cyclin Dependent Kinase (CDK2)

The paper identifies FDA-approved drugs with potential binding activity against CDK2 using computational methods and selects Carteolol as a candidate.

Cross-modal transfer enables zero-shot prediction of drug responses across cell types

GRAFT predicts drug responses across cell types without direct measurements by transferring genetic perturbation data to chemical perturbations.

Improving Molecular Docking Pose Recovery through Targeted Side-Chain Monte Carlo Sampling of an Apo Structure

The paper improves molecular docking by using targeted side-chain Monte Carlo sampling of an apo structure, achieving better ligand pose recovery than apo or restrained MD approaches.

Small molecule structure elucidation from NMR spectra with generative and reasoning models

This paper introduces an AI system for automated small molecule structure elucidation from NMR spectra, combining generative models with reasoning agents to outperform existing tools.

Bioactivity foundation model for hit identification and hit-to-lead optimization from sequence data

PSICHIC+ is a bioactivity foundation model that predicts binding affinities from sequence data, improving hit identification and hit-to-lead optimization using heterogeneous bioactivity data.

Does Short Molecular Dynamics Rescoring Improve the Ranking of p38α Inhibitors over Docking? A Replicate Benchmark

This study compares docking and short MD rescoring for ranking p38α inhibitors and finds no significant improvement from MD rescoring over docking.

Toxicity Prediction Tools Do Not Generalize to Novel Chemistry

This study evaluates toxicity prediction tools on approved drugs, finding no method outperforms simple fingerprints, and highlighting the need for inputs beyond molecular structure.

Instance-Wise Contrastive Graph Neural Network Enables the Discovery of Novel Aedes aegypti Larvicidal Compounds

This paper introduces a contrastive graph neural network to discover novel larvicidal compounds against Aedes aegypti, identifying LC-79 as a potent and selective candidate.

When Does Active Learning Improve ADMET Prediction? A Multi-Endpoint Benchmark

This paper benchmarks active learning strategies for ADMET prediction, finding benefits vary by endpoint and strategy, with Deep Ensembles showing better predictive performance than MC Dropout.

Task-Dependent Performance of GNNs and Pretrained Models in ADMET Prediction

The paper benchmarks GNNs, pretrained models, and fingerprints for ADMET prediction under fair evaluation, finding no single model dominates and highlighting the importance of endpoint-specific selection and leakage-controlled testing.

Integrative drug discovery for Parkinson disease with DIMER

The paper introduces DIMER, a framework for drug repurposing and virtual screening in Parkinson's disease, identifying 423 candidates and validating four in organoids.

Interpreting Graph Neural Networks for Antibacterial Drug Repurposing against Acinetobacter baumannii

This paper uses a graph neural network to predict antibacterial activity against Acinetobacter baumannii and identifies piperazine as a key contributor to predicted activity, leading to the testing of CE3F4.

Transcriptomic similarity guides source selection beyond tissue lineage

The paper shows that transcriptomic similarity can rank candidate sources for response prediction better than random, but does not resolve aggregation or transfer uncertainty.

NK-RCPP: a receptor-conditioned computational workflow for prioritizing neurokinin-receptor peptide candidates

The paper introduces NK-RCPP, a workflow for prioritizing neurokinin-receptor peptide candidates using receptor-matched scores and structural-neighbour comparisons.

Integrated Transcriptomic and Structural Prioritization of Sulforaphane Targets in Neurodegeneration: A Multi-Dataset In Silico Study

This study identifies potential neurodegeneration targets of sulforaphane using computational methods across multiple datasets.

Paired activity learning supports systematic identification of mutant-selective EGFR inhibitors

The paper develops a computational-experimental framework to identify mutant-selective EGFR inhibitors by leveraging paired activity measurements and structural analysis.

Pathway-Based Analysis of Essential Metabolic Enzymes as Drug Targets

This study identifies PabB as a potential drug target for tuberculosis by pathway analysis and screens FDA-approved drugs for repurposing using molecular docking.

Accelerated discovery and optimization of a potent andselective MMP12 inhibitor with the machine learningplatform CAMOLE-1

This paper describes the development of CAMOLE-1, a machine learning platform for designing potent and selective MMP12 peptide inhibitors, achieving results in about one year with 270 synthesized peptides.

Computationally Guided Design of Two Novel Phosphodiesterase-4 Inhibitors for Treatment of Plaque Psoriasis

The paper designs two novel PDE4 inhibitors for plaque psoriasis with improved ADMET properties compared to Roflumilast using computational methods.

Expanding Therapeutic Target Space from Genes to Disease-Relevant Molecular Forms with Agent3Diso

Agent3Diso identifies disease-relevant molecular forms by integrating molecular data with agentic reasoning to guide therapeutic discovery beyond gene-level targets.

How AlphaFold 3 helps with multiscale drug discovery

This study benchmarks AlphaFold 3's interaction scores against BANDOCK+ for drug discovery, showing AF3's slightly better performance in disease ranking but BANDOCK+'s broader coverage.

Integrating Multisource Target Evidence with Machine-Learning Potency Prediction for Alzheimer's Disease Drug Discovery

The paper develops a platform integrating multisource target evidence with machine learning for Alzheimer's drug discovery, improving potency prediction and model performance.

Mechanistic Insights into Adaptive Binding of Vitamins and Hormones at Hotspot-Enriched Interfaces of the Synaptotagmin 1 C2AB Domain

This study investigates the adaptive binding of seven molecules to the Synaptotagmin 1 C2AB domain using molecular docking and simulations, revealing that desolvation and structural flexibility, not just binding energy, govern ligand interactions.

Network organization of protein binding-site landscapes across scales

The paper uses network analysis of protein binding sites to reveal organizational patterns across scales, showing both diversification within protein categories and local similarities across boundaries.

MIEN: a deep-learning model for pocket-conditioned glycan pose and conformation discrimination

MIEN improves glycan pose ranking by distinguishing experimentally observed conformations from non-native ones in protein-carbohydrate interactions.

Predicting Lifespan-Extending Compounds Using Supervised Machine Learning

This study uses supervised machine learning to predict lifespan-extending compounds in C. elegans by comparing different feature representations and models.

qcGEM: a graph-based molecular representation with quantum chemistry awareness

qcGEM introduces a quantum-chemistry-aware graph-based molecular representation that improves performance and interpretability in molecule-related tasks.

Computer-Assisted Systematic Chemical-Space Mapping of a First-in-Class Peripherally Restricted α2AAR Agonist through Scaffold-Seeded Enumeration

The paper uses scaffold-seeded enumeration to explore chemical space around a first-in-class 2AAR agonist, identifying active compounds with analgesic effects.

LigRNA: High-accuracy prediction of RNA-ligand binding sites via dynamic chemical complementarity and language model encoding

LigRNA predicts RNA-ligand binding sites using a graph neural network that combines language model embeddings and physicochemical features, achieving improved performance over existing methods.

PATHNet: A Thermodynamic Path-Integral-Inspired Neural Network for Protein-Ligand Interaction Prediction

PATHNet improves protein-ligand interaction prediction by combining physics-inspired modeling with neural networks, achieving high accuracy across multiple targets.