Computational Biology Computer Aided Drug Designing

Ensemble-Based Screening for Drug Discovery

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Ensemble-Based Screening for Drug Discovery

Ensemble based virtual screening means the use of conformational ensembles from crystal structures, NMR studies or molecular dynamics simulations. It has gained greater acceptance as advances in the theoretical framework, computational algorithms, and software packages enable simulations at longer time scales. By this, we can focus on the use of computationally generated conformational ensembles and emerging methods that use these ensembles for discovery, such as the Relaxed Complex Scheme or Dynamic Pharmacophore Model. We also use the more rigorous physics-based computational techniques such as accelerated molecular dynamics and thermodynamic integration and their applications in improving conformational sampling or the ranking of virtual screening hits. Finally, technological advances that will help make virtual screening tools more accessible to a wider audience in computer aided drug design are used.

Ensemble docking means the generation of an “ensemble” of drug target conformations in computational structure-based drug discovery, usually obtained by using molecular dynamics simulation that is used in docking candidate ligands. This approach is now well established in the field of early-stage drug discovery. Some of the build methodological advances in conformational sampling will be used by the researchers that will give a historical account of the development of ensemble based docking.

The computational identification of drugs leads out of large compound libraries through receptor-based virtual screening (VS) is a well-established method to predict putative inhibitors for target receptors. Despite advances in the underlying algorithms of virtual screening experiments have incrementally improved our ability to differentiate binders from non-binders, a number of obstacles remain. A major outstanding challenge in the practice of virtual screening is the treatment of receptor flexibility, which is especially difficult owing to the many degrees of conformational freedom in target receptors. Steady increases in computational power, coupled with improvements in the underlying algorithms and available structural experimental data, are enabling a new paradigm for virtual screening, wherein computationally predicted ensembles from first-principle simulations are being used in rational drug design efforts. The integration of these more rigorous physics-based methods will have far reaching impact on translational medicine, including the ability to: 

  • better understand the structural dynamics of disease-related target receptors
  • improve our quantitative assessments of ligand-receptor interactions
  • discover novel modes of ligand binding and inhibition
  • develop new therapeutics that are patient-specific and less prone to drug resistance

Ensemble learning is a very common paradigm in ML field, where many models are trained on the same problem’s data, to combine in the end the results in one improved prediction of targets for drug discovery. 

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