Flare™ V8 릴리즈: Cresset의 CADD 워크벤치 최신 버전의 흥미로운 새로운 과학, 향상된 기능 및 시각적 분석 도구에 액세스하세요
Cresset의 CADD 워크벤치인 Flare V8에서는 리간드-단백질 복합체의 결합 자유 에너지 계산을 위한 신규 Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) 방법, ...
Computer-aided drug design (CADD) techniques are a routine part of modern drug development. Performing modeling before experimental work begins maximizes the potential to discover a molecular structure with the best therapeutic potential among the vast number of possibilities. Quantum mechanical and classical mechanical calculations can predict a molecule’s physical properties, estimate its binding strength to a target protein, and generate unexpected structural diversity while maintaining a molecule’s biological properties. However, these calculations are often too slow to be applied at a large scale.
In the next 5–10 years future innovation will be a clever blend of FEP and artificial intelligence.
Julien Michel, University of Edinburgh
One way to speed calculations is to use a set of CADD data to train machine learning models to predict molecular features of drug candidates that are slow to compute directly. Machine learning extends the capability of 3D shape and electronic structure calculations in CADD workflows to potentially operate at the scale of billions of molecules. The combination of CADD and machine learning could even one day speed resource-intensive free energy perturbation (FEP) calculations, which predict binding affinity with accuracy comparable to experimental measurements.