AIMBLE Platform for
Until very recently, AI models for drug discovery have adopted top-down approaches, i.e., statistical inference methods that are consequently not structure-based. Our machine learning (ML) models are based on the structure-based data of accurate binding affinities between proteins and ligands. Furthermore, we incorporate physics-based domain knowledge of atoms into our ML model which hence can offer a more reliable prediction.
Aimble Toxicity Prediction
Over 30% of new drug candidates fail at (pre-) clinical phase due to their toxic effects. We have developed in-house toxicity prediction technology by combining cumulative data-preprocessing skills and multi-task-trained AI machines. Specifically, a chemical compound is virtually fragmented into smaller parts and they are more elaborately encoded in the form of molecular descriptors which are then feeded to our neural network. A toxicophore can be identified via cooperative roles of these descriptors with the help of multi-criteria (hence more reasonable) decision of our neural machine.