Virtual screening identifies hits from extensive chemical space
Virtual screening (VS) is a computational technique used in drug discovery to search real or virtual libraries of small molecules in order to identify potential hit candidates.
- Search against large chemical space (from millions to billions)
- Efficient screening with high success rate
- Integrated services to provide rational starting point for Hit Identification, Lead Optimization and Medicinal Chemistry
- Reduce real laboratory experiments and accelerating the drug discovery process in a more efficient and economical way
- 3.4Bn compound ‘make-on-demand’ library provides rapid access to real samples, shipped in 4-6 weeks with successful rate of up to 80%
Virtual Library
WuXi AppTec offers access to virtual chemical spaces built from our library of novel drug-like scaffolds. Due to carefully selected building block supply and proven chemistry, these libraries consist of molecules that can be quickly synthesized on demand.
Core-Based Library
- Create reaction scheme for interested molecular scaffold (core)
- Identify/search available building block sets for reaction scheme
- Enumerate compounds for reaction scheme of each scaffold
- Create diverse compound set to form core-based focused library
Synthetically Feasible Virtual Library
The library covers rigorously validated chemical space of over 200 million virtual compounds based on optimized one-step/one-pot reactions. It is a combination of WuXi AppTec’s chemistry expertise and cutting edge software technologies, resulting in a chemical space over 200 million virtual molecules that can be mined and synthesized within a few weeks.
GalaXi (WuXi AppTec & BioSolveIT) – Virtual Space Design
Billions of accessible small molecules search with vHTS or with BioSolveIT Infinisee
Machine learning empowered virtual high-throughput screening
Advances in ML-empowered vHTS allows us to access a rapidly expanding and diverse chemical space, to discover potent novel molecules with accelerated timeline and reduced cost.
Key features of ML-empowered vHTS platform:
- Vastly expand the chemical space that is limited by real compound collections
- Ability to process large chemical spaces (from millions to billions)
- In silico screening with on-the-fly enumeration and make-on-demand virtual libraries
- Reduce real laboratory experiments and transform drug discovery in a more efficient and economical way
- Provide rational starting points for integrated services
- Evaluate target binding with high success rate of activity
- vHTS trained ML models can create generative designs to expedite hit-to-lead and lead optimization
Using output from hit-finding campaigns or other existing data, ML-empowered vHTS has shown to identify diverse, drug-like and easily-synthesizable molecules that are different from the original screening libraries or datasets
Predict phenotypic activities to discover novel antibiotics
- Deep neural network to predict antibiotic activity in molecules that are structurally different from existing antibiotics
- Compound demonstrated efficacy in mice infection model2
