Webinar Discovery and development of EP652, A METTL3 Inhibitor with efficacy in liquid and Solid Tumour ModelsWe highlight computational analysis and modeling that facilitated in-silico design; providing a potent, active and tractable METTL3 inhibitor...
Postprocessing potential hitsOur tools are not limited to only providing a score that can be used to rank ligands. We graphically represent ligands, showing their Electrostatic...
Webinar Computational approaches to prioritize macrocyclic designs for synthesisWe present a digital workflow that can be used to prioritize challenging ligands for synthesis
Case study Prioritization of new molecule design using QSAR models – 2D- and 3D-QSAR studiesIntroduction The viral main protease Mpro is a crucial enzyme for the replication of the severe acute respiratory syndrome coronavirus...
AI/MLEnhancing efficiency, empowering decisions with AI tools that enhance productivity and simplify complex processes
Case study Accelerating discovery of a METTL3 inhibitor through efficient in silico design and prioritizationCresset Discovery provides molecular modeling expertise utilizing the application of proprietary in silico methods which, when combined with...
QSAR ModelsBuild predictive Quantitative SAR models for fast and accurate activity and ADME property prediction
Article Application of CADD methods for predicting the potency of macrocyclic JAK2 inhibitors to increase success rate when pursuing challenging designs for synthesisThe development of macrocycles has received increasing interest in drug discovery as a rational approach for restricting the conformation of...
Article Enhancing QSAR Models: tackling descriptor intercorrelation with robust Gradient Boosting Machine Learning models in Flare™In a typical drug discovery scenario, we often deal with large numbers of compounds, their associated properties, and activities gathered from...
Citation Machine Learning-Based 3D-QSAR Models for Predicting the Estrogen Receptor-Binding Activity of Small MoleculesMachine Learning-Based 3D-QSAR Models for Predicting the Estrogen Receptor-Binding Activity of Small Molecules B.R, Bharath, Sreerupa Mitra,...
