Lead optimization mapper: automating free energy calculations for lead optimization
| Autor: | Jonathan P. Redmann, David L. Mobley, Teng Lin, Nathan M. Lim, Vivian R. Jaber, Yujie Wu, Christopher M. Summa, Shuai Liu, Robert Abel |
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| Rok vydání: | 2013 |
| Předmět: |
Computer science
Entropy Molecular Dynamics Simulation Ligands computer.software_genre Article Automation Consistency (database systems) Lead (geology) Drug Discovery Redundancy (engineering) Humans Trypsin Enzyme Inhibitors Physical and Theoretical Chemistry computer.programming_language Binding Sites Python (programming language) Computer Science Applications Models Chemical Automated algorithm Drug Design Factor Xa Thermodynamics Substructure Free energies Data mining Algorithm computer Algorithms Software Energy (signal processing) Factor Xa Inhibitors |
| Zdroj: | Journal of Computer-Aided Molecular Design. 27:755-770 |
| ISSN: | 1573-4951 0920-654X |
| DOI: | 10.1007/s10822-013-9678-y |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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