▪ AI candidate drug discovery.

The platform is able to design high-quality derivatives or analogues based on known active compounds, apply deep learning to generate models, match properties and conduct synthesis feasibility analysis, and quickly find high-quality lead compounds. We have made arrangements for the development of small molecule drugs and antibody drugs.

▪ AI molecular docking.

The platform has the state-of-the-art machine learning (ML) techniques in computational docking. Our molecular dynamics (MD)-based protocols are capable in estimating the free energy of binding between the ligand and target protein.

▪ AI property prediction.

The platform is able to effectively extracting structural features (including processing small molecule and protein structure) through deep neural network algorithm. It virtually predicts and evaluates ADMET and other properties of small molecule structures on cell, protein and disease level.

▪ AI virtual screening.

The platform has abundant database resources, high-performance computer servers, and can provide AI-based virtual screening services. The optimized virtual screening program can reduce the number of compounds to be experimentally screened on a large scale, increase the possibility of ideal lead discovery, shorten the virtual screening service time, and reduce the risk of failure of subsequent lead optimization.

▪ AI retrosynthetic path design.

Synthetic organic chemistry involves in drug discovery and chemical biology. Retrosynthesis is one of the most complex issues in the field of organic chemistry, which is the design of efficient synthetic routes for a given target. It is an efficient and environmental-friendly synthesis of valuable molecules with well-designed and feasible routes.