Pioneering Molecular Innovation: 

Multimodal 3D Design with Precise Pose Alignment Crafting Novel Architectures, Rationally Optimized & Pocket-Complementary Compounds. 

Unlock Dormant Data: 

Synthesize Institutional Insights into Tailored AI Copilots, Powering Context-Aware Drug Discovery.  

MolVadoTM Multimodal AI 3D Molecular Generation Model

Pocket-based 3D molecule generation 

Substructure-Constrained 3D molecule generation 

 Pocket-based 3D molecule generation: Generation of novel and conformationally reasonable drug-like molecules based on the protein binding pocket .

 Fragment-based Molecular Generation: Generation of drug-like molecules based on fixed molecular fragments. 

 Rationality Evaluation: Evaluation of the 2D structural and 3D conformational rationality of generated molecules, filtering out structurally unreasonable compounds. 

 Activity Molecule Replication: Generation of compounds similar in structure to known active molecules based on the protein binding pocket, ensuring drug-likeness of the generated molecules. 

 Support for Online Modification & Iterative Generation: Enabling one-stop modification and labeling on generated molecules, with iterative generation based on selected molecules. 

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CollectorTM Compound structure & information extraction

 Desktop-based tool, ready to use instantly: Can easily extract molecule structures in only 1s.

 High-precision molecule recognition: Support batch extraction of thousands of chemical structures in patents/documents/pictures from JPG/PNG/PDF formats with real-time correction.

 Flexible recognition and easy export: Can identify both OCSR (chemical structures) and IUPAC (standard chemical names), and enables quick one-click copying or download as SDF/XLS/SMILES/PNG.

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MD-IFP Analysis (Interaction Fingerprint analysis of Molecular Dynamics trajectories)

 Dynamic Simulation of Protein-Ligand Binding States: Overcoming the limitations of static structures, molecular dynamics simulations are employed to explore the protein-ligand binding states, revealing key conformational changes. 

 Non-Covalent Interaction Identification: Hydrogen bonds, π interactions, salt bridges, halogen bonds, and other non-covalent interactions are analyzed, with a quantitative assessment of the contributions of critical residues to ligand affinity, providing a reliable basis for structural optimization. 

 AI-Assisted Efficient Analysis: Machine learning techniques are integrated to extract key features from large-scale MD trajectories, enabling the automatic identification of the optimal binding conformations. 

 RMSD Analysis: This analysis is used to assess the stability of the ligand’s own conformation and its binding stability within the protein pocket. 

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Torsion Scan

 Original Molecular Fragmentation Algorithm: Precisely dissects the compound structure to minimize the impact of structural errors on torsional energy scanning results. 

 Quantum Chemistry Precision: Employs double-hybrid density functional theory (DFT) methods, offering torsional energy scans with chemical accuracy. 

 Unbiased Conformational Space Exploration: Through a proprietary asynchronous scanning algorithm, it eliminates systematic biases introduced by traditional single-point strategies. Combined with multi-replica molecular dynamics (MD) simulations, it accurately identifies the global energy minimum conformation. 

 High-Precision Energy Calculations: Natively supports implicit solvent models, enabling direct simulation of biologically relevant environments, bridging the accuracy gap between gas-phase calculations and condensed-phase experimental observations. 

 Torsional Energy Prediction Model: Utilizes machine learning techniques to predict molecular torsional energies, achieving quantum chemistry-level accuracy with the speed of force-field methods. 

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Substructure Search

 The system extracts and standardizes compound structures from patents and literature to build a database of over ten million small molecule drug structures. 

 Patent information coverage exceeds 95%, with more than 95% of small molecule drug patent structures included and continuous monthly updates. 

 It supports the generation of results and uploading of structure queries: based on 3D molecular generation results, users can select molecular fragments and perform one-stop retrieval of corresponding patent and literature information.

 The search results comprehensively cover key patent and literature information, including patent publication numbers, application dates, publication dates, current assignees, and literature DOI numbers. 

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