Lead optimization mapper: automating free energy calculations for lead optimization

Abstract

Alchemical free energy calculations hold increasing promise as an aid to drug discovery efforts. However, applications of these techniques in discovery projects have been relatively few, partly because of the difficulty of planning and setting up calculations. Here, we introduce Lead Optimization Mapper, LOMAP, an automated algorithm to plan efficient relative free energy calculations between potential ligands within a substantial library of perhaps hundreds of compounds. In this approach, ligands are first grouped by structural similarity primarily based on the size of a (loosely defined) maximal common substructure, and then calculations are planned within and between sets of structurally related compounds. An emphasis is placed on ensuring that relative free energies can be obtained between any pair of compounds without combining the results of too many different relative free energy calculations (to avoid accumulation of error) and by providing some redundancy to allow for the possibility of error and consistency checking and provide some insight into when results can be expected to be unreliable. The algorithm is discussed in detail and a Python implementation, based on both Schrödinger's and OpenEye's APIs, has been made available freely under the BSD license.

Is Ring Breaking Feasible in Relative Binding Free Energy Calculations?

“…2–4 However, large chemical modifications require substantially more sampling, and hence, with a fixed amount of sampling, increasing the size of the transformation can increase the magnitude of errors due to sampling. Thus, in order to ensure typical modifications are relatively small, we recently designed a program, lead optimization mapper (LOMAP), 5 for planning efficient RBFE calculations.…”

Molecular dynamics and Monte Carlo simulations for protein–ligand binding and inhibitor design

“…Finally, an important consideration is the time taken to obtain the results, both in terms of computational cost and human effort. The move to exploit the computing power of graphics processing units (GPUs) by entirely re-writing molecular dynamics algorithms has resulted in order-of-magnitude speedups [14,15], while automated workflows significantly reduce the set-up time for the calculations [16]. …”

Virtual substitution scan via single‐step free energy perturbation

“…[23][24][25][26] In recent years, considerable efforts have been made to address these issues 6,19,[27][28][29] and toolkits designed to automate the preparation, setup and analysis of free energy calculation have been developed. [30][31][32] Accompanying this methodological development is the rapid expansion of our computing power. With high performance clusters and special-purpose machines, simulations up to hundreds of nanoseconds are performed routinely, and reports of microsecond to millisecond simulations are no longer uncommon.…”