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Targeting the interaction with or displacement of the 'right' water molecule can significantly increase inhibitor potency in structure-guided drug design. Multiple computational approaches exist to predict which waters should be targeted for displacement to achieve the largest gain in potency. However, the relative success of different methods remains underexplored. Here, we present a comparison of the ability of five water prediction programs (3D-RISM, SZMAP, WaterFLAP, WaterRank, and WaterMap) to predict crystallographic water locations, calculate their binding free energies, and to relate differences in these energies to observed changes in potency. The structural cohort included nine Bruton's Tyrosine Kinase (BTK) structures, and nine bromodomain structures. Each program accurately predicted the locations of most crystallographic water molecules. However, the predicted binding free energies correlated poorly with the observed changes in inhibitor potency when solvent atoms were displaced by chemical changes in closely related compounds.

Computational chemistry/informatics scientists and software engineers in Genentech Small Molecule Drug Discovery collaborate with experimental scientists in a therapeutic project-centric environment. Our mission is to enable and improve pre-clinical drug discovery design and decisions. Our goal is to deliver timely data, analysis, and modeling to our therapeutic project teams using best-in-class software tools. We describe our strategy, the organization of our group, and our approaches to reach this goal. We conclude with a summary of the interdisciplinary skills required for computational scientists and recommendations for their training.

Small molecule distribution coefficients between immiscible nonaqueuous and aqueous phases-such as cyclohexane and water-measure the degree to which small molecules prefer one phase over another at a given pH. As distribution coefficients capture both thermodynamic effects (the free energy of transfer between phases) and chemical effects (protonation state and tautomer effects in aqueous solution), they provide an exacting test of the thermodynamic and chemical accuracy of physical models without the long correlation times inherent to the prediction of more complex properties of relevance to drug discovery, such as protein-ligand binding affinities. For the SAMPL5 challenge, we carried out a blind prediction exercise in which participants were tasked with the prediction of distribution coefficients to assess its potential as a new route for the evaluation and systematic improvement of predictive physical models. These measurements are typically performed for octanol-water, but we opted to utilize cyclohexane for the nonpolar phase. Cyclohexane was suggested to avoid issues with the high water content and persistent heterogeneous structure of water-saturated octanol phases, since it has greatly reduced water content and a homogeneous liquid structure. Using a modified shake-flask LC-MS/MS protocol, we collected cyclohexane/water distribution coefficients for a set of 53 druglike compounds at pH 7.4. These measurements were used as the basis for the SAMPL5 Distribution Coefficient Challenge, where 18 research groups predicted these measurements before the experimental values reported here were released. In this work, we describe the experimental protocol we utilized for measurement of cyclohexane-water distribution coefficients, report the measured data, propose a new bootstrap-based data analysis procedure to incorporate multiple sources of experimental error, and provide insights to help guide future iterations of this valuable exercise in predictive modeling., Competing Interests: VIII. CONFLICT OF INTEREST STATEMENTDLM and JDC are members of the Scientific Advisory Board for Schrödinger, LLC.

Structure- and property-based drug design is an integral part of modern drug discovery, enabling the design of compounds aimed at improving potency and selectivity. However, building molecules using desktop modeling tools can easily lead to poor designs that appear to form many favorable interactions with the protein's active site. Although a proposed molecule looks good on screen and appears to fit into the protein site X-ray crystal structure or pharmacophore model, doing so might require a high-energy small molecule conformation, which would likely be inactive. To help scientists make better design decisions, we have built integrated, easy-to-use, interactive software tools to perform docking experiments, de novo design, shape and pharmacophore based database searches, small molecule conformational analysis and molecular property calculations. Using a combination of these tools helps scientists in assessing the likelihood that a designed molecule will be active and have desirable drug metabolism and pharmacokinetic properties. Small molecule discovery success requires project teams to rapidly design and synthesize potent molecules with good ADME properties. Empowering scientists to evaluate ideas quickly and make better design decisions with easy-to-access and easy-to-understand software on their desktop is now a key part of our discovery process.

Using data from the in vitro liver microsomes metabolic stability assay, we have developed QSAR models to predict in vitro human clearance. Models were trained using in house high-throughput assay data reported as the predicted human hepatic clearance by liver microsomes or pCLh. Machine learning regression methods were used to generate the models. Model output for a given molecule was reported as its probability of being metabolically stable, thus allowing for synthesis prioritization based on this prediction. Use of probability, instead of the regression value or categories, has been found to be an efficient way for both reporting and assessing predictions. Model performance is evaluated using prospective validation. These models have been integrated into a number of desktop tools, and the models are routinely used to prioritize the synthesis of compounds. We discuss two therapeutic projects at Genentech that exemplify the benefits of a probabilistic approach in applying the models. A three-year retrospective analysis of measured liver microsomes stability data on all registered compounds at Genentech reveals that the use of these models has resulted in an improved metabolic stability profile of synthesized compounds.