Emerging structure-based computational methods to screen the exploding accessible chemical space.

Area of Science:

• Computational chemistry
• Drug discovery
• cheminformatics

Background:

• Structure-based virtual screening (SBVS) is crucial for identifying potential drug candidates by predicting their interaction with target proteins.
• The exponential growth of chemical libraries presents computational challenges, as screening capacity lags behind the expanding number of accessible molecules.

Purpose of the Study:

• To review novel, increasingly popular approaches for accelerating virtual screening.
• To discuss machine learning-accelerated and synthon-based library screening methods.

Main Methods:

• Review of seminal proof-of-concept studies.
• Summary of the latest developments in accelerated virtual screening techniques.
• Discussion of limitations and future research directions.

Main Results:

• Machine learning and synthon-based approaches offer solutions to computationally screen large chemical libraries more efficiently.
• These methods enhance the ability to identify high-quality compounds within massive datasets.

Conclusions:

• Accelerated virtual screening methods are vital for modern drug discovery, enabling efficient exploration of vast chemical spaces.
• Future directions involve further development and integration of these computational strategies to overcome screening bottlenecks.