Infinite Intelligence
Pharma

Insight & Innovation for Drug Discovery

深思明辨,百药可成

Infinite Intelligence
Pharma

Insight & Innovation for Drug Discovery

深思明辨,百药可成

IIPharma

Infinite Intelligence Pharma, officially launched in 2019, uses artificial intelligence technique to accelerate drug discovery process. Our drug discovery R&D platform PharmMind, which integrates human expertise, state-of-art AI algorithms and cutting-edge drug design methods are designed to addresses key problems in early stage of drug discovery.

PharmaMind

PharmaMind uses advanced artificial intelligence drug discovery (AIDD) and computer-aided drug design technology (CADD) to promote the drug discovery process. The system is driven by big data, AIDD and CADD technology. Besides accuracy and speed, interpretability is also a key consideration to assist experts in understanding problems and making decisions.

PharmaMind

PharmaMind uses advanced artificial intelligence drug discovery (AIDD) and computer-aided drug design technology (CADD) to promote the drug discovery process. The system is driven by big data, AIDD and CADD technology. Besides accuracy and speed, interpretability is also a key consideration to assist experts in understanding problems and making decisions.

Technology

分子探索3D
MolExplorer3D

分子优化3D
MolOptimizer3D

分子搜索
MolSearcher

分子评估
MolEvaluator

分子探索3D
MolExplorer3D

分子优化3D
MolOptimizer3D

分子搜索
MolSearcher

分子评估
MolEvaluator

虚拟筛选
MolDOCKingVS

逆合成分析
MolSynthesize

互动分子设计
MolInteractive

分子数据挖掘
MolDataMiner

虚拟筛选
MolDOCKingVS

逆合成分析
MolSynthesize

互动分子设计
MolInteractive

分子数据挖掘
MolDataMiner

分子探索3D
MolExplorer3D
  • Stimulate your inspiration by effectively explore compound scaffolds that can potentially bind to specific targets.
  • Based on the target structure, use deep learning technology to generate molecules with good drug-like properties and high potential binding ability.
分子优化3D
MolOptimizer3D
  • Optimize the structure of hit/lead-based on binding affinity, druggability, and synthesis feasibility.
  • Improve biological activity as well as pharmacokinetic properties.
  • Reduce toxicity to obtain safe and effective drug candidates for subsequent development.
分子优化3D
MolOptimizer3D
  • Optimize the structure of hit/lead-based on binding affinity, druggability, and synthesis feasibility.
  • Improve biological activity as well as pharmacokinetic properties.
  • Reduce toxicity to obtain safe and effective drug candidates for subsequent development.
分子搜索
MolSearcher
  • Search other potential active small molecules based on reference ligands.
  • Use multi-dimensional similarity index based on deep learning to expand the search of chemistry space.
分子评估
MolEvaluator

Use a variety of models that integrate artificial intelligence and computer-aided drug design to evaluate the multi-dimensional properties of a compound

    • Physical and chemical properties
    • ADMET properties
    • Druggability
    • Synthesis feasibility
    • Docking scoring and pharmacophore matching scoring

etc.

分子评估
MolEvaluator

Use a variety of models that integrate artificial intelligence and computer-aided drug design to evaluate the multi-dimensional properties of a compound

    • Physical and chemical properties
    • ADMET properties
    • Druggability
    • Synthesis feasibility
    • Docking scoring and pharmacophore matching scoring

etc.

虚拟筛选
MolDOCKingVS
  • Virtual screening with artificial intelligence can quickly screen ultra-large-scale compound libraries and increase the probability of obtaining highly active compounds.
逆合成分析
MolSynthesize
  • The synthesis process of new molecules either by generation or modification is not easy. Therefore, solving the synthesis problems of the designed molecules is the key step for actual verification.
  • Reverse synthesis analysis based on fast heuristic enhanced tree search algorithm helps synthesizers arrange the order of experiments and obtain compounds faster.
  • Evaluating the synthesis feasibility and potential synthesis methods of compounds.
逆合成分析
MolSynthesize
  • The synthesis process of new molecules either by generation or modification is not easy. Therefore, solving the synthesis problems of the designed molecules is the key step for actual verification.
  • Reverse synthesis analysis based on fast heuristic enhanced tree search algorithm helps synthesizers arrange the order of experiments and obtain compounds faster.
  • Evaluating the synthesis feasibility and potential synthesis methods of compounds.
互动分子设计
MolInteractive
  • The incorporation of human expertise and artificial intelligence via a well-designed interactive molecular design process is an ideal way to improve the success rate of design, which makes the design process stable and controllable.
分子数据挖掘
MolDataMiner 
  • Use combined advanced deep learning technology (natural language processing technology, image recognition technology, graph neural network technology, etc.) to build a knowledge graph.
分子数据挖掘
MolDataMiner 
  • Use combined advanced deep learning technology (natural language processing technology, image recognition technology, graph neural network technology, etc.) to build a knowledge graph.

团队成员

裴剑锋 博士

裴剑锋 博士

首席科学家

郝天龙 博士

郝天龙 博士

新药研发负责人

团队成员

徐优俊 博士

徐优俊 博士

联合创始人

吴国振 博士

吴国振 博士

生物研发负责人

About us

张伟林 博士

张伟林 博士

联合创始人

龚超骏 博士

龚超骏 博士

苏州研发负责人

团队成员

t us

肖晋川 博士

肖晋川 博士

IT负责人

About us

裴剑锋 博士

创始人

徐优俊 博士

首席技术官

张伟林 博士

首席科学家

肖晋川 博士

IT负责人

郝天龙 博士

新药研发负责人

吴国振 博士

生物研发负责人

龚超骏 博士

苏州研发负责人