Graph machine learning (GML) is receiving growing interest within the pharmaceutical and
biotechnology industries for its ability to model biomolecular structures, the functional …

Combination therapy has shown an obvious efficacy on complex diseases and can greatly
reduce the development of drug resistance. However, even with high-throughput screens …

Artificial intelligence (AI) is poised to transform therapeutic science. Therapeutics Data
Commons is an initiative to access and evaluate AI capability across therapeutic modalities …

Combinatorial therapies have been recently proposed to improve the efficacy of anticancer
treatment. The SynergyFinder R package is a software used to analyze pre-clinical drug …

Therapeutics machine learning is an emerging field with incredible opportunities for
innovatiaon and impact. However, advancement in this field requires formulation of …

Large language models (LLMs) have been shown to have significant potential in few-shot
learning across various fields, even with minimal training data. However, their ability to …

Abstract CellMiner Cross-Database (CellMinerCDB, discover. nci. nih. gov/cellminercdb)
allows integration and analysis of molecular and pharmacological data within and across …

Abstract Background Protein-protein interactions (PPIs) are central to many biological
processes. Considering that the experimental methods for identifying PPIs are time …

Drug combinations have demonstrated high efficacy and low adverse side effects compared
to single drug administration in cancer therapies and thus have drawn intensive attention …

Application of machine and deep learning methods in drug discovery and cancer research
has gained a considerable amount of attention in the past years. As the field grows, it …