Artificial Intelligence and Deep learning expecting to solve Free Energy Perturbation
Once a technique predominantly utilized and developed by academics, the widespread adoption of free energy simulations into industrial computer-aided drug design is now on the horizon.
The holy grail of in silico techniques is to be able to predict how tightly an inhibitor will bind to its target. Computational chemists, largely based in academia, have grappled with this for decades, devising numerous so-called ‘free-energy’ techniques in the hope of accurately predicting ligand binding. One such technique is Free Energy Perturbation (FEP), which has had something of a renaissance in the last couple of years.
FEP seeks to calculate the difference in binding affinity between two ligands rather than their absolute affinities; something which is considerably more computationally efficient and also more reliable. The adoption of FEP into commercial software packages such as Schrödinger’s FEP+ represented something of a watershed in the field, allowing CADD scientists to use these tools in earnest on their own targets.
Like any computational method, the results obtained are always limited by the description of the underlying physics. With the adoption of the method into industrial pharmaceutical research, however, there will inevitably be a drive to improve these descriptions, and as such it is likely that the use of FEP will only increase in the coming years.
Source: http://eureka.criver.com/whats-hot-in-2017-computer-aided-drug-design/
