Tiago Rodrigues

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.

Drug Screen Targeted at Plasmodium Liver Stages Identifies a Potent Multistage Antimalarial Drug

Plasmodium parasites undergo a clinically silent and obligatory developmental phase in the host’s liver cells before they are able to infect erythrocytes and cause malaria symptoms. To overcome the scarcity of compounds targeting the liver stage of malaria, we screened a library of 1037 existing drugs for their ability to inhibit Plasmodium hepatic development. Decoquinate emerged as the strongest inhibitor of Plasmodium liver stages, both in vitro and in vivo. Furthermore, decoquinate kills the parasite’s replicative blood stages and is active against developing gametocytes, the forms responsible for transmission. The drug acts by selectively and specifically inhibiting the parasite’s mitochondrial bc1 complex, with little cross-resistance with the antimalarial drug atovaquone. Oral administration of a single dose of decoquinate effectively prevents the appearance of disease, warranting its exploitation as a potent antimalarial compound.

Combining On‐Chip Synthesis of a Focused Combinatorial Library with Computational Target Prediction Reveals Imidazopyridine GPCR Ligands

Using the example of the Ugi three-component reaction we report a fast and efficient microfluidic-assisted entry into the imidazopyridine scaffold, where building block prioritization was coupled to a new computational method for predicting ligand-target associations. We identified an innovative GPCR-modulating combinatorial chemotype featuring ligand-efficient adenosine A1/2B and adrenergic α1A/B receptor antagonists. Our results suggest the tight integration of microfluidics-assisted synthesis with computer-based target prediction as a viable approach to rapidly generate bioactivity-focused combinatorial compound libraries with high success rates.

Inhibitors of the mitochondrial electron transport chain and de novo pyrimidine biosynthesis as antimalarials: The present status.

Malaria is a major worldwide public health threat with worrying social and economic burdens due to the rapid emergence of multidrug-resistant Plasmodium falciparum strains. As a result, there is an urgent need to find novel drugs that might overcome clinical resistance to marketed antimalarials. In recent years, the mitochondrial electron transport chain (mtETC) has been explored for the development of new antimalarials. Type II NADH:quinone oxidoreductase (PfNDH2), succinate dehydrogenase (SDH) and cytochrome bc1 have become a major focus of those efforts, leading to several studies of its biochemistry and the design of potent inhibitors. Furthermore, de novo pyrimidine biosynthesis in malaria parasites, particularly dihydroorotate dehydrogenase (PfDHODH), is also receiving increasing attention. The enzymes involved in the mtETC are valuable targets in malaria chemotherapy, not only because they play a critical role in metabolic pathways of P. falciparum, but also because they differ significantly from the analogous mammalian system. Inhibition of such enzymes results in the shutdown of mitochondrial electron flow, leading to the arrest of pyrimidine biosynthesis and consequent parasite death. In this review, we aim to outline recent advances in the inhibition of mitochondrial metabolic pathways, highlighting the major classes of known inhibitors and those that are currently being developed.

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

We report a multi-objective deu2005novo 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.

Steering target selectivity and potency by fragment-based de novo drug design

Kinase inhibitors: Ligand-based de novo design is validated as a viable technology for rapidly generating innovative compounds possessing the desired biochemical profile. The study discloses the discovery of the most selective vascular endothelial growth factor receptor-2 (VEGFR-2) kinase inhibitor (right in scheme) known to date as prime lead for antiangiogenic drug development.

From Complex Natural Products to Simple Synthetic Mimetics by Computational De Novo Design

We present the computational deu2005novo design of synthetically accessible chemical entities that mimic the complex sesquiterpene natural product (-)-Englerinu2005A. We synthesized lead-like probes from commercially available building blocks and profiled them for activity against a computationally predicted panel of macromolecular targets. Both the design template (-)-Englerinu2005A and its low-molecular weight mimetics presented nanomolar binding affinities and antagonized the transient receptor potential calcium channel TRPM8 in a cell-based assay, without showing target promiscuity or frequent-hitter properties. This proof-of-concept study outlines an expeditious solution to obtaining natural-product-inspired chemical matter with desirable properties.

Fragment-Based De Novo Design Reveals a Small-Molecule Inhibitor of Helicobacter Pylori HtrA†

Sustained identification of innovative chemical entities is key for the success of chemical biology and drug discovery. We report the fragment-based, computer-assisted deu2005novo design of a small molecule inhibiting Helicobacter pylori HtrA protease. Molecular binding of the designed compound to HtrA was confirmed through biophysical methods, supporting its functional activity in vitro. Hit expansion led to the identification of the currently best-in-class HtrA inhibitor. The results obtained reinforce the validity of ligand-based deu2005novo design and binding-kinetics-guided optimization for the efficient discovery of pioneering lead structures and prototyping drug-like chemical probes with tailored bioactivity.

De Novo Fragment Design for Drug Discovery and Chemical Biology.

Automated molecular deu2005novo design led to the discovery of an innovative inhibitor of death-associated protein kinaseu20053 (DAPK3). An unprecedented crystal structure of the inactive DAPK3 homodimer shows the fragment-like hit bound to the ATP pocket. Target prediction software based on machine learning models correctly identified additional macromolecular targets of the computationally designed compound and the structurally related marketed drug azosemide. The study validates computational deu2005novo design as a prime method for generating chemical probes and starting points for drug discovery.