Lead Finder

About

The original docking algorithm integrated into Lead Finder can be tailored for either quick but less accurate virtual screening applications or slower but more in-depth analyses.

Lead Finder is used by computational and medicinal chemists for drug discovery, as well as pharmacologists and toxicologists involved in in silico assessment of ADME-Tox properties. Additionally, it is used by biochemists and enzymologists working on modelling protein-ligand interactions, enzyme specificity, and rational enzyme design. Lead Finder's specialization in ligand docking and binding energy estimation is a result of its advanced docking algorithm and the precision with which it represents protein-ligand interactions.

Docking algorithm

From a mathematical perspective, ligand docking involves modelling a multidimensional surface that describes the free energy associated with protein-ligand binding. This surface can be highly complex, with ligands possessing as many as 15-20 degrees of freedom, such as freely rotatable bonds.

Lead Finder's approach combines genetic algorithm search, local optimization techniques, and knowledge gathered during the search process.

Scoring function

The Lead Finder scoring function represents protein-ligand interactions more precisely. The scoring function's model considers various types of molecular interactions.

In this scoring function, individual energy contributions are carefully adjusted with empirically derived coefficients tailored to objectives. Such as the prediction of binding energies, the ranking of energy for docked ligand poses, and the ordering of active and inactive compounds during virtual screening experiments. To achieve these goals, Lead Finder employs three types of scoring functions, based on the same set of energy contributions but with different sets of energy-scaling coefficients.

Docking success rate

Docking success rate was benchmarked as a percentage of correctly docked ligands for a set of protein-ligand complexes extracted from PDB. Results showed root mean squared deviations of 2 Å or less for 80-96% of the structures in the respective test sets (FlexX, Glide SP, Glide XP, Gold, LigandFit, MolDock, Surflex).

References

References

1. "Benchmarking Lead Finder's Performance in Virtual Screening".
2. Novikov, Fedor N.. (2012-05-09). "Lead Finder docking and virtual screening evaluation with Astex and DUD test sets". Journal of Computer-Aided Molecular Design.
3. Stroganov O. (2008). "Lead Finder: An Approach To Improve Accuracy of Protein−Ligand Docking, Binding Energy Estimation, and Virtual Screening". J. Chem. Inf. Model..
4. (1997). "Multiple automatic base selection: Protein-ligand docking based on incremental construction without manual intervention". J Comp Aid Mol Des.
5. (2004). "Glide: A New Approach for Rapid, Accurate Docking and Scoring. 1. Method and Assessment of Docking Accuracy". Journal of Medicinal Chemistry.
6. (2006). "Glide: extra Precision Glide: Docking and Scoring incorporating a Model of Hydrophobic Enclosure for Protein-Ligand Complexes". Journal of Medicinal Chemistry.
7. (1997). "Development and Validation of a Genetic Algorithm for Flexible Docking". J Mol Biol.
8. (2007). "Diverse, High-Quality Test Set for the Validation of Protein-Ligand Docking Performance". Journal of Medicinal Chemistry.
9. (2002). "A New Test Set for Validating Predictions of Protein-Ligand Interaction". Proteins: Structure, Function, and Genetics.
10. (2003). "LigandFit: a novel method for the shape-directed rapid docking of ligands to protein active sites". J Mol Graph Model.
11. (2006). "MolDock: A new technique for high-accuracy molecular docking". Journal of Medicinal Chemistry.
12. A. N. Jain. (2003). "Surflex: Fully Automatic Flexible Molecular Docking Using a Molecular Similarity-Based Search Engine". Journal of Medicinal Chemistry.