Toward in Silico Modeling of Dynamic Combinatorial Libraries.

Dynamic combinatorial libraries (DCLs) display adaptive behavior, enabled by the reversible generation of their molecular constituents from building blocks, in response to external effectors, e.g., protein receptors. So far, chemoinformatics has not yet been used for the design of DCLs-which comprise a radically different set of challenges compared to classical library design. Here, we propose a chemoinformatic model for theoretically assessing the composition of DCLs in the presence and the absence of an effector. An imine-based DCL in interaction with the effector human carbonic anhydrase II (CA II) served as a case study. Support vector regression models for the imine formation constants and imine-CA II binding were derived from, respectively, a set of 276 imines synthesized and experimentally studied in this work and 4350 inhibitors of CA II from ChEMBL. These models predict constants for all DCL constituents, to feed software assessing equilibrium concentrations. They are publicly available on the dedicated website. Models rationally selected two amines and two aldehydes predicted to yield stable imines with high affinity for CA II and provided a virtual illustration on how effector affinity regulates DCL members.

Pros and cons of virtual screening based on public "Big Data": In silico mining for new bromodomain inhibitors.

The Virtual Screening (VS) study described herein aimed at detecting novel Bromodomain BRD4 binders and relied on knowledge from public databases (ChEMBL, REAXYS) to establish a battery of predictive models of BRD activity for in silico selection of putative ligands. Beyond the actual discovery of new BRD ligands, this represented an opportunity to practically estimate the actual usefulness of public domain "Big Data" for robust predictive model building. Obtained models were used to virtually screen a collection of 2 million compounds from the Enamine company collection. This industrial partner then experimentally screened a subset of 2992 molecules selected by the VS procedure for their high likelihood to be active. Twenty nine confirmed hits were detected after experimental testing, representing 1% of the selected candidates. As a general conclusion, this study emphasizes once more that public structure-activity databases are nowadays key assets in drug discovery. Their usefulness is however limited by the state-of-the-art knowledge harvested so far by published studies. Target-specific structure-activity information is rarely rich enough, and its heterogeneity makes it extremely difficult to exploit in rational drug design. Furthermore, published affinity measures serving to build models selecting compounds to be experimentally screened may not be well correlated with the experimental hit selection criterion (in practice, often imposed by equipment constraints). Nevertheless, a robust 2.6-fold increase in hit rate with respect to an equivalent, random screening campaign showed that machine learning is able to extract some real knowledge in spite of all the noise in structure-activity data.

Virtual Screening with Generative Topographic Maps: How Many Maps Are Required?

Universal generative topographic maps (GTMs) provide two-dimensional representations of chemical space selected for their "polypharmacological competence", that is, the ability to simultaneously represent meaningful activity and property landscapes, associated with many distinct targets and properties. Several such GTMs can be generated, each based on a different initial descriptor vector, encoding distinct structural features. While their average polypharmacological competence may indeed be equivalent, they nevertheless significantly diverge with respect to the quality of each property-specific landscape. In this work, we show that distinct universal maps represent complementary and strongly synergistic views of biologically relevant chemical space. Eight universal GTMs were employed as support for predictive classification landscapes, using more than 600 active/inactive ligand series associated with as many targets from the ChEMBL database (v.23). For nine of these targets, it was possible to extract, from the Directory of Useful Decoys (DUD), truly external sets featuring sufficient "actives" and "decoys" not present in the landscape-defining ChEMBL ligand sets. For each such molecule, projected on every class landscape of a particular universal map, a probability of activity was estimated, in analogy to a virtual screening (VS) experiment. Cross-validated (CV) balanced accuracy on landscape-defining ChEMBL data was unable to predict the success of that landscape in VS. Thus, the universal map with best CV results for a given property should not be prioritized as the implicitly best predictor. For a given map, predictions for many DUD compounds are not trustworthy, according to applicability domain considerations. By contrast, simultaneous application of all universal maps, and rating of the likelihood of activity as the mean returned by all applicable maps, significantly improved prediction results. Performance measures in consensus VS using multiple maps were always superior or similar to those of the best individual map.

Sweetness prediction of natural compounds.

Based on the most exhaustive database of sweeteners with known sweetness values, a new quantitative structure-activity relationship model for sweetness prediction has been set up. Analysis of the physico-chemical properties of sweeteners in the database indicates that the structure of most potent sweeteners combines a hydrophobic scaffold functionalized by a limited number of hydrogen bond sites (less than 4 hydrogen bond donors and 10 acceptors), with a moderate molecular weight ranging from 350 to 450g·mol-1. Prediction of sweetness, bitterness and toxicity properties of the largest database of natural compounds have been performed. In silico screening reveals that the majority of the predicted natural intense sweeteners comprise saponin or stevioside scaffolds. The model highlights that their sweetness potency is comparable to known natural sweeteners. The identified compounds provide a rational basis to initiate the design and chemosensory analysis of new low-calorie sweeteners.