We introduce the QuanSA method for inducing physically meaningful field-based models of
ligand binding pockets based on structure-activity data alone. The method is closely related …

We present a probabilistic data fusion framework that combines multiple computational
approaches for drawing relationships between drugs and targets. The approach has special …

Binding affinity prediction is frequently addressed using computational models constructed
solely with molecular structure and activity data. We present a hybrid structure-guided …

Tuberculosis (TB) is a devastating disease that claims millions of lives every year. Hindered
access or non‐compliance to medication, especially in developing countries, led to drug …

The rational drug discovery process has limited success despite all the advances in
understanding diseases, and technological breakthroughs. Indeed, the process of drug …

Surflex-QMOD integrates chemical structure and activity data to produce physically-realistic
models for binding affinity prediction. Here, we apply QMOD to a 3D-QSAR benchmark …

The population segment aged 65 and older is rapidly increasing. As the population
continues to age, the demand for long-term health care services, such as skilled nursing …

This DMIN project is based on the Pastoral Ministry program at Nyack College, which
publicly promotes that it leads the student to become a professional in the ministry context …

Résumé Le processus de découverte de médicaments a un succès limité malgré tous les
progrès réalisés. En effet, on estime actuellement que le développement d'un médicament …

The structured nature of chemical data means machine learning models trained to predict
protein-ligand binding risk overfitting the data, impairing their ability to generalise and make …