Daniel Reker

Counting on natural products for drug design

Natural products and their molecular frameworks have a long tradition as valuable starting points for medicinal chemistry and drug discovery. Recently, there has been a revitalization of interest in the inclusion of these chemotypes in compound collections for screening and achieving selective target modulation. Here we discuss natural-product-inspired drug discovery with a focus on recent advances in the design of synthetically tractable small molecules that mimic natures chemistry. We highlight the potential of innovative computational tools in processing structurally complex natural products to predict their macromolecular targets and attempt to forecast the role that natural-product-derived fragments and fragment-like natural products will play in next-generation drug discovery.

Identifying the macromolecular targets of de novo-designed chemical entities through self-organizing map consensus

Significance New chemical entities (NCEs) with desired pharmacological and biological activity spectra fuel drug discovery and provide tools for chemical biologists. Computer-assisted molecular design generates novel chemotypes with predictable polypharmacologies. We present the successful application of fully automated de novo drug design coupled with a pioneering approach for target panel prediction to obtain readily synthesizable bioactive compounds. This innovative concept enabled the identification of relevant macromolecular targets of computationally designed NCEs and led to the discovery of previously unknown off-targets of approved drugs. De novo molecular design and in silico prediction of polypharmacological profiles are emerging research topics that will profoundly affect the future of drug discovery and chemical biology. The goal is to identify the macromolecular targets of new chemical agents. Although several computational tools for predicting such targets are publicly available, none of these methods was explicitly designed to predict target engagement by de novo-designed molecules. Here we present the development and practical application of a unique technique, self-organizing map–based prediction of drug equivalence relationships (SPiDER), that merges the concepts of self-organizing maps, consensus scoring, and statistical analysis to successfully identify targets for both known drugs and computer-generated molecular scaffolds. We discovered a potential off-target liability of fenofibrate-related compounds, and in a comprehensive prospective application, we identified a multitarget-modulating profile of de novo designed molecules. These results demonstrate that SPiDER may be used to identify innovative compounds in chemical biology and in the early stages of drug discovery, and help investigate the potential side effects of drugs and their repurposing options.

Chemically Advanced Template Search (CATS) for Scaffold-Hopping and Prospective Target Prediction for ‘Orphan’ Molecules

Drug discovery is driven by the identification of new chemical entities (NCEs).1,2 Virtual screening and de novo design techniques have been proven to serve this purpose, thereby complementing experimental biochemical and biological approaches.3 Still, it remains a matter of debate, which particular molecular representation and similarity index are preferable for a given drug target in order to identify appropriate NCEs with minimal synthetic and testing effort involved.4 Ligand-based chemical similarity approaches have been effectively applied to large-scale activity and target prediction for known drugs, some of the prominent methods being PASS developed by Poroikov et al.,5 the techniques conceived by Mestres and co-workers,6 and the similarity ensemble approach (SEA) implemented by the Shoichet group.7 Here, we compared several popular two-dimensional molecular representations for their ability to retrieve actives (enrichment potential) and chemotypes (scaffold-hopping potential) from a collection of druglike bioactive compounds. Subsequently the applied chemical advanced template search (CATS)8 was applied to predicting potential drug targets for a virtually assembled combinatorial compound library, from which we synthesized and successfully tested candidate compounds. The results demonstrate that CATS is not only suited for its intended purpose of NCE retrieval by scaffold-hopping,9 but also for reliable target profiling of ‘orphan’ virtual molecules.10 It thereby complements the suite of available validated tools for target prediction.

Common non-epigenetic drugs as epigenetic modulators

Epigenetic effects are exerted by a variety of factors and evidence increases that common drugs such as opioids, cannabinoids, valproic acid, or cytostatics may induce alterations in DNA methylation patterns or histone conformations. These effects occur via chemical structural interactions with epigenetic enzymes, through interactions with DNA repair mechanisms. Computational predictions indicate that one-twentieth of all drugs might potentially interact with human histone deacetylase, which was prospectively experimentally verified for the compound with the highest predicted interaction probability. These epigenetic effects add to wanted and unwanted drug effects, contributing to mechanisms of drug resistance or disease-related and unrelated phenotypes. Because epigenetic changes might be transmitted to offspring, the need for reliable and cost-effective epigenetic screening tools becomes acute.

Active-learning strategies in computer-assisted drug discovery

High-throughput compound screening is time and resource consuming, and considerable effort is invested into screening compound libraries, profiling, and selecting the most promising candidates for further testing. Active-learning methods assist the selection process by focusing on areas of chemical space that have the greatest chance of success while considering structural novelty. The core feature of these algorithms is their ability to adapt the structure-activity landscapes through feedback. Instead of full-deck screening, only focused subsets of compounds are tested, and the experimental readout is used to refine molecule selection for subsequent screening cycles. Once implemented, these techniques have the potential to reduce costs and save precious materials. Here, we provide a comprehensive overview of the various computational active-learning approaches and outline their potential for drug discovery.

Multidimensional De Novo Design Reveals 5-HT2B Receptor-Selective Ligands†

We report a multi-objective de novo design study driven by synthetic tractability and aimed at the prioritization of computer-generated 5-HT2B receptor ligands with accurately predicted target-binding affinities. Relying on quantitative bioactivity models we designed and synthesized structurally novel, selective, nanomolar, and ligand-efficient 5-HT2B modulators with sustained cell-based effects. Our results suggest that seamless amalgamation of computational activity prediction and molecular design with microfluidics-assisted synthesis enables the swift generation of small molecules with the desired polypharmacology.

Revealing the Macromolecular Targets of Fragment-Like Natural Products.

Fragment-like natural products were identified as ligand-efficient chemical matter for hit-to-lead development and chemical-probe discovery. Relying on a computational method using a topological pharmacophore descriptor and a drug database, several macromolecular targets from distinct protein families were expeditiously retrieved for structurally unrelated chemotypes. The selected fragments feature structural dissimilarity to the reference compounds and suitable target affinity, and they offer opportunities for chemical optimization. Experimental confirmation of hitherto unknown macromolecular targets for the selected molecules corroborate the usefulness of the computational approach and suggests broad applicability to chemical biology and molecular medicine.

Deorphaning Pyrrolopyrazines as Potent Multi‐Target Antimalarial Agents

The discovery of pyrrolopyrazines as potent antimalarial agents is presented, with the most effective compounds exhibiting EC50 values in the low nanomolar range against asexual blood stages of Plasmodium falciparum in human red blood cells, and Plasmodium berghei liver schizonts, with negligible HepG2 cytotoxicity. Their potential mode of action is uncovered by predicting macromolecular targets through avant-garde computer modeling. The consensus prediction method suggested a functional resemblance between ligand binding sites in non-homologous target proteins, linking the observed parasite elimination to IspD, an enzyme from the non-mevalonate pathway of isoprenoid biosynthesis, and multi-kinase inhibition. Further computational analysis suggested essential P. falciparum kinases as likely targets of our lead compound. The results obtained validate our methodology for ligand- and structure-based target prediction, expand the bioinformatics toolbox for proteome mining, and provide unique access to deciphering polypharmacological effects of bioactive chemical agents.

Chemography of Natural Product Space

We present the application of the generative topographic map algorithm to visualize the chemical space populated by natural products and synthetic drugs. Generative topographic maps may be used for nonlinear dimensionality reduction and probabilistic modeling. For compound mapping, we represented the molecules by two-dimensional pharmacophore features (chemically advanced template search descriptor). The results obtained suggest a close resemblance of synthetic drugs with natural products in terms of their pharmacophore features, despite pronounced differences in chemical structure. Generative topographic map-based cluster analysis revealed both known and new potential activities of natural products and drug-like compounds. We conclude that the generative topographic map method is suitable for inferring functional similarities between these two classes of compounds and predicting macromolecular targets of natural products.

De novo design and optimization of Aurora A kinase inhibitors

Drug discovery programs urgently seek new chemical entities that meet the needs of a demanding pharmaceutical industry. Consequently, de novo ligand design is currently re-emerging as a novelty-generating approach. Using ligand-based de novo design software, we computationally generated, chemically synthesized and biochemically tested a new arylsulfonamide against Aurora A kinase, a validated drug target in several types of cancer. The designed compound exhibited desired direct inhibitory activity against Aurora A kinase. By chemical optimization we obtained a lead structure exhibiting sustained activity in phenotypic assays. These results emphasize the potential of ligand-based de novo design to consistently deliver functional new chemotypes within short timeframes and limited effort.