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      Octanol–water partition coefficient measurements for the SAMPL6 blind prediction challenge

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          Abstract

          Partition coefficients describe the equilibrium partitioning of a single, defined charge state of a solute between two liquid phases in contact, typically a neutral solute. Octanol-water partition coefficients ( K ow ), or their logarithms (log P ), are frequently used as a measure of lipophilicity in drug discovery. The partition coefficient is a physicochemical property that captures the thermodynamics of relative solvation between aqueous and nonpolar phases, and therefore provides an excellent test for physics-based computational models that predict properties of pharmaceutical relevance such as protein-ligand binding affinities or hydration/solvation free energies. The SAMPL6 Part II Octanol-Water Partition Coefficient Prediction Challenge used a subset of kinase inhibitor fragment-like compounds from the SAMPL6 p K a Prediction Challenge in a blind experimental benchmark. Following experimental data collection, the partition coefficient dataset was kept blinded until all predictions were collected from participating computational chemistry groups. A total of 91 submissions were received from 27 participating research groups. This paper presents the octanol-water log P dataset for this SAMPL6 Part II Partition Coefficient Challenge, which consisted of 11 compounds (six 4-aminoquinazolines, two benzimidazole, one pyrazolo[3,4-d]pyrimidine, one pyridine, one 2-oxoquinoline substructure containing compounds) with log P values in the range of 1.95–4.09. We describe the potentiometric log P measurement protocol used to collect this dataset using a Sirius T3, discuss the limitations of this experimental approach, and share suggestions for future log P data collection efforts for the evaluation of computational methods.

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          The influence of lipophilicity in drug discovery and design.

          The role of lipophilicity in drug discovery and design is a critical one. Lipophilicity is a key physicochemical property that plays a crucial role in determining ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties and the overall suitability of drug candidates. There is increasing evidence to suggest that control of physicochemical properties such as lipophilicity, within a defined optimal range, can improve compound quality and the likelihood of therapeutic success. This review focuses on understanding lipophilicity, techniques used to measure lipophilicity, and summarizes the importance of lipophilicity in drug discovery and development, including a discussion of its impact on individual ADMET parameters as well as its overall influence on the drug discovery and design process, specifically within the past 15 years. A current review of the literature reveals a continued reliance on the synthesis of novel structures with increased potency, rather than a focus on maintaining optimal physicochemical properties associated with ADMET throughout drug optimization. Particular attention to the optimum region of lipophilicity, as well as monitoring of lipophilic efficiency indices, may contribute significantly to the overall quality of candidate drugs at different stages of discovery.
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            Overview of the SAMPL5 host-guest challenge: Are we doing better?

            The ability to computationally predict protein-small molecule binding affinities with high accuracy would accelerate drug discovery and reduce its cost by eliminating rounds of trial-and-error synthesis and experimental evaluation of candidate ligands. As academic and industrial groups work toward this capability, there is an ongoing need for datasets that can be used to rigorously test new computational methods. Although protein-ligand data are clearly important for this purpose, their size and complexity make it difficult to obtain well-converged results and to troubleshoot computational methods. Host-guest systems offer a valuable alternative class of test cases, as they exemplify noncovalent molecular recognition but are far smaller and simpler. As a consequence, host-guest systems have been part of the prior two rounds of SAMPL prediction exercises, and they also figure in the present SAMPL5 round. In addition to being blinded, and thus avoiding biases that may arise in retrospective studies, the SAMPL challenges have the merit of focusing multiple researchers on a common set of molecular systems, so that methods may be compared and ideas exchanged. The present paper provides an overview of the host-guest component of SAMPL5, which centers on three different hosts, two octa-acids and a glycoluril-based molecular clip, and two different sets of guest molecules, in aqueous solution. A range of methods were applied, including electronic structure calculations with implicit solvent models; methods that combine empirical force fields with implicit solvent models; and explicit solvent free energy simulations. The most reliable methods tend to fall in the latter class, consistent with results in prior SAMPL rounds, but the level of accuracy is still below that sought for reliable computer-aided drug design. Advances in force field accuracy, modeling of protonation equilibria, electronic structure methods, and solvent models, hold promise for future improvements.
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              Predicting small-molecule solvation free energies: an informal blind test for computational chemistry.

              Experimental data on the transfer of small molecules between vacuum and water are relatively sparse. This makes it difficult to assess whether computational methods are truly predictive of this important quantity or merely good at explaining what has been seen. To explore this, a prospective test was performed of two different methods for estimating solvation free energies: an implicit solvent approach based on the Poisson-Boltzmann equation and an explicit solvent approach using alchemical free energy calculations. For a set of 17 small molecules, root mean square errors from experiment were between 1.3 and 2.6 kcal/mol, with the explicit solvent free energy approach yielding somewhat greater accuracy but at greater computational expense. Insights from outliers and suggestions for future prospective challenges of this kind are presented.
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                Author and article information

                Journal
                Journal of Computer-Aided Molecular Design
                J Comput Aided Mol Des
                Springer Science and Business Media LLC
                0920-654X
                1573-4951
                April 2020
                December 19 2019
                April 2020
                : 34
                : 4
                : 405-420
                Article
                10.1007/s10822-019-00271-3
                7301889
                31858363
                c23eca71-a1f0-4ea0-b36a-ac0b3d5eade6
                © 2020

                http://www.springer.com/tdm

                http://www.springer.com/tdm

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