AIMC Topic: Drug Discovery

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T-ALPHA: A Hierarchical Transformer-Based Deep Neural Network for Protein-Ligand Binding Affinity Prediction with Uncertainty-Aware Self-Learning for Protein-Specific Alignment.

Journal of chemical information and modeling
There is significant interest in targeting disease-causing proteins with small molecule inhibitors to restore healthy cellular states. The ability to accurately predict the binding affinity of small molecules to a protein target in silico enables the...

Artificial Intelligence for Drug Discovery: An Update and Future Prospects.

Seminars in nuclear medicine
Artificial intelligence (AI) has become a pivotal tool for medical image analysis, significantly enhancing drug discovery through improved diagnostics, staging, prognostication, and response assessment. At a high level, AI-driven image analysis enabl...

Emerging horizons of AI in pharmaceutical research.

Advances in pharmacology (San Diego, Calif.)
Artificial Intelligence (AI) has revolutionized drug discovery by enhancing data collection, integration, and predictive modeling across various critical stages. It aggregates vast biological and chemical data, including genomic information, protein ...

Identifying RNA-small Molecule Binding Sites Using Geometric Deep Learning with Language Models.

Journal of molecular biology
RNAs are emerging as promising therapeutic targets, yet identifying small molecules that bind to them remains a significant challenge in drug discovery. This underscores the crucial role of computational modeling in predicting RNA-small molecule bind...

Artificial Intuition and accelerating the process of antimicrobial drug discovery.

Computers in biology and medicine
New drug development is a very challenging, expensive, and usually time-consuming process. This issue is very important with regard to antimicrobials, which are affected by the global issue of the development and spread of resistance. This framework ...

A diagnostic model for sepsis using an integrated machine learning framework approach and its therapeutic drug discovery.

BMC infectious diseases
BACKGROUND: Sepsis remains a life-threatening condition in intensive care units (ICU) with high morbidity and mortality rates. Some biomarkers commonly used in clinic do not have the characteristics of rapid and specific growth and rapid decline afte...

AI-enabled language models (LMs) to large language models (LLMs) and multimodal large language models (MLLMs) in drug discovery and development.

Journal of advanced research
BACKGROUND: Due to the recent revolution of artificial intelligence (AI), AI-enabled large language models (LLMs) have flourished and started to be applied in various sectors of science and medicine. Drug discovery and development are time-consuming,...

metaCDA: A Novel Framework for CircRNA-Driven Drug Discovery Utilizing Adaptive Aggregation and Meta-Knowledge Learning.

Journal of chemical information and modeling
In the emerging field of RNA drugs, circular RNA (circRNA) has attracted much attention as a novel multifunctional therapeutic target. Delving deeper into the intricate interactions between circRNA and disease is critical for driving drug discovery e...

A novel neural network-based nearest neighbor approach for drug function prediction from chemical structures.

European journal of pharmacology
Drug function prediction is a crucial task in drug discovery, design, and development, which involves the prediction of the biological functions of a drug molecule based on its chemical structure. Misleading drug function is a common reason for adver...

AGDIFF: Attention-Enhanced Diffusion for Molecular Geometry Prediction.

Journal of chemical information and modeling
Accurate prediction of molecular geometries is crucial for drug discovery and materials science. Existing fast conformer prediction algorithms often rely on approximate empirical energy functions, resulting in low accuracy. More accurate methods like...