The hepatic organic anion transporting polypeptides (OATPs) influence the pharmacokinetics of several drug classes and are involved in many clinical drug–drug interactions. Predicting potential interactions with OATPs is, therefore, of value. Here, we developed in vitro and in silico models for identification and prediction of specific and general inhibitors of OATP1B1, OATP1B3, and OATP2B1. The maximal transport activity (MTA) of each OATP in human liver was predicted from transport kinetics and protein quantification. We then used MTA to predict the effects of a subset of inhibitors on atorvastatin uptake in vivo. Using a data set of 225 drug-like compounds, 91 OATP inhibitors were identified. In silico models indicated that lipophilicity and polar surface area are key molecular features of OATP inhibition. MTA predictions identified OATP1B1 and OATP1B3 as major determinants of atorvastatin uptake in vivo. The relative contributions to overall hepatic uptake varied with isoform specificities of the inhibitors.

The inhibitor specificities of P-gp, BCRP and MRP2 were shown to be highly overlapping. General ABC inhibitors were more lipophilic and aromatic than specific inhibitors and non-inhibitors. The identified specific inhibitors can be used to delineate transport processes in complex experimental systems, whereas the multi-specific inhibitors are useful in primary ABC transporter screening in drug discovery settings.

Conformal prediction is introduced as an alternative approach to domain applicability estimation. The advantages of using conformal prediction are as follows: First, the approach is based on a consistent and well-defined mathematical framework. Second, the understanding of the confidence level concept in conformal predictions is straightforward, e.g. a confidence level of 0.8 means that the conformal predictor will commit, at most, 20% errors (i.e., true values outside the assigned prediction range). Third, the confidence level can be varied depending on the situation where the model is to be applied and the consequences of such changes are readily understandable, i.e. prediction ranges are increased or decreased, and the changes can immediately be inspected. We demonstrate the usefulness of conformal prediction by applying it to 10 publicly available data sets.

The liver-specific organic cation transport protein (OCT1; SLC22A1) transports several cationic drugs including the antidiabetic drug metformin and the anticancer agents oxaliplatin and imatinib. In this study, we explored the chemical space of registered oral drugs with the aim of studying the inhibition pattern of OCT1 and of developing predictive computational models of OCT1 inhibition. In total, 191 structurally diverse compounds were examined in HEK293-OCT1 cells. The assay identified 47 novel inhibitors and confirmed 15 previously known inhibitors. The enrichment of OCT1 inhibitors was seen in several drug classes including antidepressants. High lipophilicity and a positive net charge were found to be the key physicochemical properties for OCT1 inhibition, whereas a high molecular dipole moment and many hydrogen bonds were negatively correlated to OCT1 inhibition. The data were used to generate OPLS-DA models for OCT1 inhibitors; the final model correctly predicted 82% of the inhibitors and 88% of the noninhibitors of the test set.

Drug discovery is a rigorous process that requires billion dollars of investments and decades of research to bring a molecule "from bench to a bedside". While virtual docking can significantly accelerate the process of drug discovery, it ultimately lags the current rate of expansion of chemical databases that already exceed billions of molecular records. This recent surge of small molecules availability presents great drug discovery opportunities, but also demands much faster screening protocols. In order to address this challenge, we herein introduce Deep Docking (DD), a novel deep learning platform that is suitable for docking billions of molecular structures in a rapid, yet accurate fashion. The DD approach utilizes quantitative structure−activity relationship (QSAR) deep models trained on docking scores of subsets of a chemical library to approximate the docking outcome for yet unprocessed entries and, therefore, to remove unfavorable molecules in an iterative manner. The use of DD methodology in conjunction with the FRED docking program allowed rapid and accurate calculation of docking scores for 1.36 billion molecules from the ZINC15 library against 12 prominent target proteins and demonstrated up to 100-fold data reduction and 6000-fold enrichment of high scoring molecules (without notable loss of favorably docked entities). The DD protocol can readily be used in conjunction with any docking program and was made publicly available.

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Macrocycles are of increasing interest as chemical probes and drugs for intractable targets like protein-protein interactions, but the determinants of their cell permeability and oral absorption are poorly understood. To enable rational design of cell-permeable macrocycles, we generated an extensive data set under consistent experimental conditions for more than 200 non-peptidic, de novo-designed macrocycles from the Broad Institute's diversity-oriented screening collection. This revealed how specific functional groups, substituents and molecular properties impact cell permeability. Analysis of energy-minimized structures for stereo- and regioisomeric sets provided fundamental insight into how dynamic, intramolecular interactions in the 3D conformations of macrocycles may be linked to physicochemical properties and permeability. Combined use of quantitative structure-permeability modeling and the procedure for conformational analysis now, for the first time, provides chemists with a rational approach to design cell-permeable non-peptidic macrocycles with potential for oral absorption.

The aim of this study was to devise experimental protocols and computational models for the prediction of intestinal drug permeability. Both the required experimental and computational effort and the accuracy and quality of the resulting predictions were considered. In vitro intestinal Caco-2 cell monolayer permeabilities were determined both in a highly accurate experimental setting (Pc) and in a faster, but less accurate, mode (Papp). Computational models were built using four different principles for generation of molecular descriptors (atom counts, molecular mechanics calculations, fragmental, and quantum mechanics approaches) and were evaluated for their ability to predict intestinal membrane permeability. A theoretical deconvolution of the polar molecular surface area (PSA) was also performed to facilitate the interpretation of this composite descriptor and allow the calculation of PSA in a simplified and fast mode. The results indicate that it is possible to predict intestinal drug permeability from rather simple models with little or no loss of accuracy. A new, fast computational model, based on partitioned molecular surface areas, that predicts intestinal drug permeability with an accuracy comparable to that of time-consuming quantum mechanics calculations is presented.