1. 1
Mohamed A. Khamis, Walid Gomaa, Walaa F. Ahmed,
Machine learning in computational docking, Artificial Intelligence In Medicine (2015),
http://dx.doi.org/10.1016/j.artmed.2015.02.002
http://dx.doi.org/10.1016/j.artmed.2015.02.002
2. Objective
http://dx.doi.org/10.1016/j.artmed.2015.02.0022
The objective of this paper is to highlight the state-of-the-art
machine learning (ML) techniques in computational docking.
The use of smart computational methods in the life cycle of
drug design is relatively a recent development that has
gained much popularity and interest over the last few years.
Computational docking is the process of predicting the best
pose (orientation + conformation) of a small molecule (drug
candidate) when bound to a target larger receptor molecule
(protein) in order to form a stable complex molecule.
3. Background
3
• Background for protein-ligand interactions:
Physical, chemical, and biological
• Molecular data formats:
e.g., .mol, .pdb, .sdf, etc.
• Docking software programs:
e.g., AutoDock, eHiTS, iDock, etc.
• Molecular databases:
Containing data of proteins with their possible ligands
e.g., PDB, PDBbind, Binding DB, DUD etc.
http://dx.doi.org/10.1016/j.artmed.2015.02.002
4. 4
ligand (small drug molecule)large protein molecule stable complex molecule
Fitting Puzzle Pieces
Drug Design: Docking of Ligand with Target Protein
Binding Site
http://dx.doi.org/10.1016/j.artmed.2015.02.002
5. Protein
HIV-1 protease (hsg1.pdb)
5
Ligand (Drug)
Indinavir (ind.pdb)
Formula:C36H47N5O4
Indinavir (IDV; trade name Crixivan,
manufactured by Merck) is inhibitor used
to treat HIV infection and AIDS.
http://dx.doi.org/10.1016/j.artmed.2015.02.002
6. Complex molecule: Indinavir when fit into binding pocket of
receptor protein HIV-1 protease
6 http://dx.doi.org/10.1016/j.artmed.2015.02.002
7. Traditional Drug Design Methods
7
• Traditional drug design techniques - such as random screening and
chance discovery are essentially trial and error methods.
• And so they are very time consuming (10-15 years), very
expensive ($300M), with extremely low yield.
• For instance, over last 50 years, 500,000 compounds have been
tested for anti-cancer;
Only 25 are in wide use today [1].
• On other hand, CADD is target specific, structure-based,
automatic, fast, and very low cost with high success rate.
1. Denny, William A., New Zealand Institute of Chemistry, The Design and
development of anti-cancer drugs. Available at
http://nzic.org.nz/ChemProcesses/biotech/12J.pdf.
http://dx.doi.org/10.1016/j.artmed.2015.02.002
8. Scoring Function
8
Is mathematical predictive model that produces a
score that represents binding free energy and
hence stability of resulting complex molecule.
Generally, such function should produce set of credible
ligands ranked according to their binding stability
along with their binding poses
X-Score: Wang R, Lai L, Wang S. Further development and validation of
empirical scoring functions for structure-based binding affinity
prediction. J Computer-Aided Molecular Design 2002;16:11–26.
http://dx.doi.org/10.1016/j.artmed.2015.02.002
9. Powers of Scoring Functions
9
Scoring Power: Score protein-ligand complex;
correlation coefficient between predicted &
experimentally determined binding affinity.
Ranking Power: Rank different ligands bound to
same target protein; successful ranking percentage.
Docking Power: Identify native binding pose among
computer-generated decoys.
Screening Power: Classification; True binders vs.
Negative Binders (random molecules).
http://dx.doi.org/10.1016/j.artmed.2015.02.002
10. Classical Scoring functions
10
Classical scoring functions e.g., X-Score rely only on fixed set of
molecular features (e.g., energy terms)
Summed in linear weighted manner that fails to model non-linear
relationships among individual energy terms.
In addition, weights of those individual energy terms are calibrated
based on specific protein family (using linear regression),
Hence, classical scoring functions are more prone to over-fitting.
http://dx.doi.org/10.1016/j.artmed.2015.02.002
11. Machine Learning-based Scoring functions
11
Ballester PJ. Machine learning approaches to predicting protein-ligand
binding. Presentation; Cambridge Computational Biology Institute - European
Molecular Biology Laboratory EMBL-EBI; Cambridge, United Kingdom; 2013.
Value to be
predicted
using
regression
techniques
http://dx.doi.org/10.1016/j.artmed.2015.02.002
12. Training & Testing sets of PDBbind v. 2007
12
Ballester PJ. Machine learning approaches to predicting protein-ligand
binding. Presentation; Cambridge Computational Biology Institute - European
Molecular Biology Laboratory EMBL-EBI; Cambridge, United Kingdom; 2013.
http://dx.doi.org/10.1016/j.artmed.2015.02.002
13. Results
http://dx.doi.org/10.1016/j.artmed.2015.02.00213
We survey this paradigm shift elaborating on the main building
components of ML approaches used in molecular docking.
For instance, the best random forest (RF)-based scoring function
(Li, 2014) on PDBbind v2007 achieves a Pearson correlation
coefficient between the predicted and experimentally
determined binding affinities of 0.803 while the best classical
scoring function achieves 0.644 (Cheng, 2009).
The best RF-based ranking power (Ashtawy, 2012) ranks the
ligands correctly based on their experimentally determined
binding affinities with accuracy 62.5% and identifies the top
binding ligand with accuracy 78.1%.
14. Conclusion
14
Machine Learning techniques give ability to utilize as
many relevant molecular features (e.g., geometric
features, pharmacophore features, etc.) as possible.
Particularly, ensemble-based machine learning
approaches (e.g., random forest, boosted
regression trees, etc.) are resilient to over fitting.
Yield good results not only on training complexes but on
any testing complexes as well.
http://dx.doi.org/10.1016/j.artmed.2015.02.002
15. Acknowledgement
15
This work is supported:
Mainly by Information Technology Industry
Development Agency (ITIDA) under ITAC Program
grant number CFP#58
In part by E-JUST Research Fellowship
http://dx.doi.org/10.1016/j.artmed.2015.02.002
16. Publications
16
Mohamed A. Khamis, Walid Gomaa, 2015,
Comparative Assessment of Scoring and Ranking
Powers of Machine-Learning-Based Scoring
Functions on an Updated Benchmark PDBbind
2013, Engineering Applications of Artificial Intelligence,
Elsevier. (submitted)
Mohamed A. Khamis, Walid Gomaa, Basem Galal, 2015,
Deep Learning Competes Random Forest in
Computational Docking, IEEE/ACM Transactions on
Computational Biology and Bioinformatics. (submitted)
http://dx.doi.org/10.1016/j.artmed.2015.02.002
