A tool for predicting the three-dimensional structure of protein-protein complexes, providing clues for the study of protein structure and function.

A 3D structure prediction tool for antigen-antibody complexes based on deep learning algorithms. Based on the predicted structures, energy contributions of the interfacial contact residues are calculated, which may provide clues for CDR optimization.

The three-dimensional structure prediction tool of protein-small molecule complexes based on deep learning algorithm can obtain the interaction information between compounds and specific residues according to the three-dimensional structure, and provide clues for the structure optimization of small molecules.

A virtual screening tool based on deep learning algorithms to rapidly screen chemically diverse small molecule libraries to obtain a list of potentially active molecules.

Given a target molecule, a built-in library of target structures is screened to obtain potential targets that the molecule may bind to.

According to a specific disease, a built-in target related to the disease can be selected, and molecular docking is performed for some or all of the disease-related targets.

According to the signaling pathway, the built-in target related to the pathway can be selected, and molecular docking can be carried out for some or all of the targets related to the pathway.

Users can establish a custom target set according to their own targets of interest, and perform systematic molecular docking.

Predict the three-dimensional structure of the target protein (POI), E3 ligase, and PROTAC ternary complex, and evaluate the rationality of linker fragments of different lengths to form PROTAC molecules.

Compound synthesis route design based on deep learning algorithm, given target compound to generate synthesis route.

HNMR prediction based on experimental data

Prediction of boiling point at different pressures (0.5-760mmHg)

Protein-RNA Complex Structure Prediction Tool

Protein-DNA Complex Structure Prediction Tool