Towards a more inductive world for drug repurposing approaches
Drug–target interaction (DTI) prediction is a challenging albeit essential task in drug repurposing. Learning on graph models has drawn special a…
Tag: machine learning
Deep lead optimization enveloped in protein pocket and its application in designing potent and selective ligands targeting LTK protein
Optimizing the chemical structure of promising drug candidates through systematic modifications to improve potency and physiochemical properties is…
Scalable and robust DNA-based storage via coding theory and deep learning
The global data sphere is expanding exponentially, projected to hit 180 zettabytes by 2025, whereas current technologies are not anticipated to s…
Large language models for scientific discovery in molecular property prediction
Large language models (LLMs) are a form of artificial intelligence system encapsulating vast knowledge in the form of natural language. These syste…
Mapping the gene space at single-cell resolution with gene signal pattern analysis
In single-cell sequencing analysis, several computational methods have been developed to map the cellular state space, but little has been done to …
Multimodal learning for mapping genotype–phenotype dynamics
How complex phenotypes emerge from intricate gene expression patterns is a fundamental question in biology. Integrating high-content genotyping app…
SVLearn: a dual-reference machine learning approach enables accurate cross-species genotyping of structural variants
Structural variations (SVs) are diverse forms of genetic alterations and drive a wide range of human diseases. Accurately genotyping SVs, particula…
A data-driven generative strategy to avoid reward hacking in multi-objective molecular design
Molecular design using data-driven generative models has emerged as a promising technology, impacting various fields such as drug discovery and the…