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These issues are removed with the new ‘adaptive Lambda scheduling’ where a fast (50ps) pre-calculation at the start of the FEP run determines the optimal number of Lambda windows required for each particular perturbation, so you don’t have to (Figure 2 – right).
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Compares under-sampling (9 Lambda windows, left) versus over-sampling (21 Lambda windows, middle) versus the adaptive Lambda scheduling (12 Lambda windows, right) for the link between 20~1h1q in one of the benchmarks CDK2.
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Unless you choose exactly right, either way is wasteful in terms of calculation time.įigure 2. If you over-sample with more than the required Lambda windows, more than sufficient overlap is reached at the cost of a more expensive calculation (Figure 2 - middle). If you under-sample and there is insufficient overlap between Lambdas the results need to be discarded and re-run (Figure 2 - left). If you were to decide the Lambda windows for each link yourself (of which there may be hundreds) you cannot know ahead of the calculation if you are over or underestimating the Lambda windows. It can be non-obvious and time-consuming to set the Lambda windows manually, and setting a blanket default number can be wasteful when some perturbations are trickier than others. Lambda windows between perturbations represent the incremental jumps made as the ligand is transformed from A to B. Adaptive Lambda Scheduling provides a ~30% speed enhancement Most obviously from this example, the change in Lambda assignments has been a big improvement. Number of ligands, links and perturbations remain the same for this example, but the difference in Lambda windows allows for a >4x speedup.Ī combination of improvements that have enabled this fast FEP run-time are discussed below. Compare total GPU hours for Flare V5 (left) versus Flare V4 (right) for a TYK2 Flare FEP benchmark run. Tooltip pop-up for Flare FEP calculations. In ‘real time’, if you have a 10 GPU cluster (Flare FEP calculations require GPUs which support OpenCL or NVIDIA CUDA), this is equivalent to about 10.6 hours for 11 molecules versus 47 hours, which makes a vast improvement for the time it takes to see results.įigure 1. For the ‘TYK2’ example from the Wang et al benchmark sets, (including 11 ligands, 14 links and 28 perturbations) this equates to 106 GPU hours in Flare V5 versus 470 GPU hours in Flare V4. Calculations in Flare V5 are now, for medium sized systems, at least 2.5-3.5x faster than Flare V4.
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The recent release of Flare V5 offers a significant improvement in the speed of Flare FEP simulations. Vast improvement in speed reduces time to see your FEP results Below I describe the speed, accuracy, and ease of use which research chemists can expect from Flare FEP, which is available for evaluation on request. Flare FEP, a component of the Flare ligand and structure-based design platform, significantly lowers this barrier as very accurate FEP results are found quickly and easily. Therefore, FEP can have the reputation to be expensive in terms of both user and total computational time. However, common barriers to using FEP are typically that the calculations are very involved, i.e., difficult to setup and execute, and can also take a long time to run. This accuracy can be better than 1 kcal/mol from experimental values, so can make a significant difference in successfully selecting the molecules with the most potential for further development and synthesis.
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Free Energy Perturbation (FEP) calculations arm the drug hunter with highly accurate binding affinity predictions for ligands.