Latest Articles for Drug Discovery

A new method enhances molecular learning by considering atom relationships.

A new method accelerates finding valuable items in large datasets.

UniCorn integrates diverse pre-training methods for effective molecular representation learning.

Blending quantum tech and NMR for molecular insights.

New AI model LDMol enhances molecule generation using text descriptions.

New methods enhance quality and diversity in scientific data collection.

Exploring how proteins change shape and function in biological processes.

A new framework improves molecular property predictions with limited data.

Discover how machine learning transforms kinase research and drug discovery.

CryoSPHERE improves protein shape reconstruction from cryo-EM images using machine learning.

A new method enhances protein functions for industry and medicine.

Recent research improves predictions of bio-molecules with transition metals for drug discovery.

FusionDTI improves predictions of drug-target interactions for enhanced drug development.

UdanDTI improves predictions of how drugs interact with proteins.

A new framework enhances drug synthesis by addressing existing method limitations.

Neural wave functions and Pfaffians enhance quantum chemistry predictions significantly.

COMPASS method addresses noise issues in molecular docking, enhancing drug discovery.

A novel approach enhances aptamer search while reducing data dependency.

Exploring the role of active learning in drug discovery through molecular docking.

New methods improve molecular simulations and interactions for better accuracy.

A new framework improves molecular graph generation techniques for drug discovery.

CBGBench offers a structured approach to enhance drug design and evaluation.

A fresh approach improves understanding of drug effects on cells.

InvMSAFold generates diverse protein sequences while preserving structural integrity.

qFit-ligand improves understanding of ligand shapes and protein interactions.

TrustAffinity enhances drug binding predictions and assesses uncertainty effectively.

A new approach using knowledge enhances efficiency in learning from relational graphs.

A new approach fine-tunes diffusion models for better user preference alignment.

Advancements in genetic techniques help uncover gene functions in harmful bacteria.

Scientists utilize quantum machine learning to simulate molecular behaviors accurately and efficiently.

T-Hop framework leverages path information for better predictions in drug discovery.

A look into how Bayesian optimization addresses high-dimensional challenges.

A fresh look at optimization challenges involving probabilities.

New generative model improves drug candidate creation using deep learning techniques.

FragLlama adapts language models for innovative molecular design and drug discovery.

Exploring quantum methods for calculating molecular energies using hydrogen as a model.

Research uncovers shared pathways in genetic and idiopathic Parkinson's disease.

A new dataset accelerates molecular property predictions for drug design.

Researchers use speculative decoding to enhance speed and efficiency in chemical predictions.

New approaches in drug design prioritize molecule synthesizability for better outcomes.