Accurate estimation of protein–ligand binding affinity is the cornerstone of computer-aided drug design. We present a universal physics-based scoring function, named SQM2.20, addressing key terms of ...
Variational graph encoders effectively combine graph convolutional networks with variational autoencoders, and have been widely employed for biomedical graph-structured data. Lam and colleagues ...
An interdisciplinary research team from Leipzig University and the Saxon AI center ScaDS.AI has developed a novel approach that integrates artificial intelligence (AI) methods with biophysical ...
University of Arkansas chemist receives $1.5 million NIH award to improve computer-aided drug design
A new $1.5 million award from the National Institutes of Health will allow a University of Arkansas chemist to develop mathematical models to improve the reliability and efficiency of computer-aided ...
Optibrium, a leading developer of software and AI solutions for drug discovery, today announced the acquisition of BioPharmics LLC, expanding its 3D drug design and modelling offering. Bringing ...
A team of Vanderbilt researchers has created a new series of drug candidates against a hard-to-target receptor involved in the formation of blood clots. The research, spearheaded by the labs of Jens ...
How are we improving the way the field of drug discovery creates machine learning algorithms to predict a protein’s interactions with a small molecule? The drug development pipeline is a costly and ...
The drug development pipeline is a costly and lengthy process. Identifying high-quality "hit" compounds—those with high potency, selectivity, and favorable metabolic properties—at the earliest stages ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results