In the area of deep-scale AI+ biopharmaceuticals, Langhui Technology has built 18 high-quality data sets covering the entire life cycle of drug development, ranging from target detection, compound screening, ADMT prediction to clinical trials, drug repositioning, and providing full-link data support for the AI pharmaceutical model from early detection to post-market evaluation.
From AlphaFold 3 to AI Design Medicine Clinic III, from Protein Design to Drug Repositioning, AI is remodeling the drug paradigm
DeepMind publishes AlphaFold 3, which achieves the first prediction of a protein + RNA+DNA+ small molecular composition combined structure, far more accurate than AlphaFold 2. Nature 2025 reviews this as a "new era of structural biology" and reduces the structure of drug targets from months to minutes.
Insilico Medicine's AI design special pulmonary fibrosis drug, INS 018_055, entered clinical phase IIa and displayed positive data, becoming the first global AI candidate for design and clinical entry. Science commented that AI pharmaceuticals moved from conceptual validation to clinical landing.
Models such as RFdiffusion, ProteinMPNN, ESM3 have been designed to design a new protein that does not exist in nature. The study published by Cell 2025 demonstrates that AI designed to combine protein proton protege and force beyond natural antibodies, and to open a new paradigm for biopharmaceutical development.
AI analyses the real world data and molecular characteristics of big amounts, providing clues for new uses of old drugs. A study published by NEJM 2025 shows that AI repositioned to identify Baretini for the treatment of COVID-19 serious diseases, from hypothetical to clinical validation only six months, reflecting the clinical value of AI repositioning.
The multi-group megamodels (scGPT, Geneformer, BioGPT) identify new target points from single-cell sequencing, gene expression spectrum. NAture Biotechnology 2026 studies show that new targets identified by AI show potential for difficult diseases such as Alzheimer ' s disease, non-alcoholic fatty liver, etc.
The in-depth learning-based ADMT prediction model has an accurate rate of overestimation of side effects such as hepatotoxicity, heart toxicity (HERG), kidney toxicity, etc. The Lancet 2026 paper stressed that AID drug safety predictions can reduce clinical trial failure rates by about 30%.
Build a high-quality data infrastructure that covers the life cycle of drug research and development and empowers AI pharmaceutical innovation
200 million + compound molecular structure, bioactive data to support virtual screening and molecular generation
50+ADMT endpoint, 1 million+molecule experimental data, predicting drug metabolic and toxicity
100,000+Data for clinical trials covering full I-IV process, including entry/end-of-service/bad events
Drug-disease linkages based on real world data, supporting new excavations of old drugs
Data cooperation mechanisms have been established with the top scientific institutes of 30+ innovative drug companies in China, CRO and Shanghai Institute of Medicine at the Chinese Academy of Medicine, and the Chinese University of Pharmacy to ensure the authority, coverage and professional designation of data sources.
Doctorate in Pharmacy, expert examination in Pharmacology/Pharmacology, AAA Auxiliary Quality Control System, Level 3 system to ensure the professional and accurateness of target-formulation data, ADMT labels, clinical end-of-doctoral interpretation.
Strictly following NMPA, FDA, EMA Data Management Guidelines for Drug Research and Development and GCP (Merticultural Quality Management Guidelines for Drug Clinical Trials), all clinical data are de-identified and have a complete authorized link.
The self-study pharmacological dedicated pedestal platform supports molecular structure visualization, SMILES automatic verification, target-formulation mapping, end-of-clinical trial interpretation aids, and human synergy increases the marking efficiency by three to five times.
By R & D stage, covering the four main sections of drug discovery, ADMT, clinical trials, drug repositioning
200 million + compound molecular structure (SMILES/InChI) with ZINC, ChEMBL, PubChem Multisource Integration
500,000 + target-format pairs, marked with IC50-Ki/Kd experimental values and integration points
200,000+Protein3D Structure (PDB format), containing AlphaFold Forecast and Experimental Parsing Structure
15 million + compound-target bioactivity records covering various types of action such as inhibition/activation/coupling
50+ADMT endpoint, 1 million+molecule experimental data, covering solubility/permeability/resiliency
Multiple endpoint toxicity data, such as hepatotoxicity/cardiology (HERG)/renal toxicity/reproductive toxicity
CYP450 E.S. metabolic stability, metabolic product identification, drug-drug interaction data
100,000+registration of clinical trials covering I-IV period, including entry/exclusion criteria/endpoint indicators
50,000 + report on clinical trial results, including main endpoint/secondary endpoint/bad event statistics
2 million + patient real world drug data to support post-market evaluation and precision medical care
FDA FAERS database structurally deficient incident report, 5 million plus bad incident record
A network of drug-disease links based on literature mining and real world evidence to support new uses of old drugs
Multi-target drug network, predicting drug detargeting and repositioning opportunities
Global complete information on listed drugs, including adaptive/role mechanisms/agent types/paths
50,000+ Structured text of drug instructions with a degree of adaptability/use/nox/negative reaction
2 million + drug-drug interactions, mechanism of effect/severity/clinical advice
Drugs respond to biomarker data, supporting precision drug use and accompanying diagnostic development
300,000 + molecular structure and biological activity of natural products supporting modernization of Chinese medicine and discovery of new drugs
View the label structure and illustrative data of the different types of pharmaceutical data set
The core application scene supported by the Langhui Medicine Data Set
Based on multi-group data and knowledge maps, AI identifies new targets and validates the drug properties
Screening of candidate molecules from the billion-grade compound reservoir or generation of a completely new molecular structure from scratch
Early prediction of drug metabolism and toxicity, reducing risk of post-clinical failure
Digging new adaptations for already marketed drugs, reducing the research and development cycle and costs
AI-assisted patient entry screening, end-point prediction, trial design optimization
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A common question answer for the Long Hye Medicine Data Set
Langhui Technology strictly follows the data management requirements of regulatory bodies such as NMPA (National Drug Authority), FDA and EMA, and the GCP (Quality Management Guidelines for Drug Clinical Trials).
Target-format data are derived from authoritative public databases such as ChEMBL, BindingDB, PDFbind and internal experimental data in collaboration with pharmaceutical companies.
Supports various standard molecular formats: SMILES (linear molecular expression), InCHI/InchiKey (international chemical marking), MOL/SDF (molecule structure document), PDB (protein 3D structure), CIF (crystalcology information file).
Rang Huitech provides customized data services that collect, integrate, and label drug data in specific disease areas according to the client’s research orientation (e.g. tumors, neurodegenerative diseases, metabolic diseases, etc.). We have a network of 30+ companies working with research institutes that respond quickly to customized needs, with a normal 6-10 week cycle from data collection to the delivery of markers.
We will provide the corresponding data authorization program based on your use scene (academic research/business research/development/education training) and sign the Data Use Agreement (DUA) to ensure data use compliance and intellectual property protection.
Langhui Technology Medicine Dataset, providing high-quality, compliant and trusted data support for AI+ biopharmaceutical innovation