Alchemical free energy methods for drug discovery: Progress and challenges

By John D. Chodera, David L. Mobley, Michael R. Shirts, R. Dixon, K. Branson, Vijay S. Pande. 2011

538 citations recorded by the source corpus.

Improved rational drug design methods are needed to lower the cost and increase the success rate of drug discovery and development. Alchemical binding free energy calculations, one potential tool for rational design, have progressed rapidly over the last decade, but still fall short of providing robust tools for pharmaceutical engineering. Recent studies, especially on model receptor systems, have clarified many of the challenges that must be overcome for robust predictions of binding affnity to be useful in rational design. In this review, inspired by a recent joint academic/industry meeting organized by the authors, we discuss these challenges and suggest a number of promising approaches for overcoming them.

Subfields: Computational Theory and Mathematics, Molecular Biology

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Citations in the corpus

Ligand biological activity predicted by cleaning positive and negative chemical correlations (2019, background): “However, determining the receptor structure and parameterizing protein–ligand interactions are often challenging, and notwithstanding those challenges, it is still computationally intensive to compute the protein–ligand binding free energy (9, 10).”
Using Rule-Based Labels for Weak Supervised Learning: A ChemNet for Transferable Chemical Property Prediction (2017, background): “[7] Therefore, using neural networks to predict such computable properties with similar accuracy will potentially lead to several orders of magnitude speed up compared to traditional computational chemistry simulations that typically take on the order of minutes to hours for each calculation.”
Insights into Protein–Ligand Interactions: Mechanisms, Models, and Methods (2016, methodology): “For case studies using these methods as well as their challenges and improvements made to them, the reader is referred to [192] and references therein.”
The future of molecular dynamics simulations in drug discovery (2011, background): “To do so, it is necessary for the ﬁeld to begin a shift from a research focus to an engineering focus’’ [76].”
FreeSolv: A database of experimental and calculated hydration free energies, with input files (2014, unknown): “Our approach to calculating these typically involves alchemical free energy calculations based on classical molecular dynamics (MD) simulations [5, 7, 29, 48], usually with a fixed-charge force field in explicit solvent.”
In silico methods and tools for drug discovery (2021, background): “However, this may also increase the computational burden, which limits simulations longer than a microsecond [195].”
Many-Body Coarse-Grained Interactions Using Gaussian Approximation Potentials. (2016, background): “This can be due to different types of atoms, corresponding to a “transmutation” or alchemical transformation of one molecule into another [61], or due to a transition along a reaction coordinate [62].”
Predicting binding free energies: Frontiers and benchmarks (2016, methodology): “The second general approach computes the difference between the binding free energies of two different ligands for the same receptor, by computing the work of artificially converting one ligand into another, first in the bound state and then free in solution [24, 27, 114, 175].”
SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical Properties (2017, methodology): “[7] 2.4 Data Preparation length of the SMILES string directly impacts the compute resources required to train RNN models.”