Drugs by numbers: reaction-driven de novo design of potent and selective anticancer leads

Iridium-catalyzed enantioselective synthesis of chiral γ-amino alcohols and intermediates of (S)-duloxetine, (R)-fluoxetine, and (R)-atomoxetine

Chiral γ-amino alcohols are the prevalent structural motifs and building blocks in pharmaceuticals and bioactive molecules. Enantioselective hydrogenation of β-amino ketones provides a straightforward and powerful tool for the synthesis of chiral γ-amino alcohols, but the asymmetric transformation is synthetically challenging. Here, a series of tridentate ferrocene-based phosphine ligands bearing modular and tunable unsymmetrical vicinal diamine scaffolds were designed, synthesized, and evaluated in the iridium-catalyzed asymmetric hydrogenation of β-amino ketones. The system was greatly effective to substrates with flexible structure and functionality, and diverse β-tertiary-amino ketones and β-secondary-amino ketones were hydrogenated smoothly. The excellent reactivities and enantioselectivities were achieved in the asymmetric delivery of various chiral γ-amino alcohols with up to 99% yields, >99% ee values, and turnover number (TON) of 48,500. The gram-scale reactions with low catalyst loading showed the potential application in industrial synthesis of chiral drugs, such as (S)-duloxetine, (R)-fluoxetine, and (R)-atomoxetine.

The hunt for antimitotic agents: an overview of structure-based design strategies

INTRODUCTION

Structure-based drug discovery offers a rational approach for the design and development of novel anti-mitotic agents which target specific proteins involved in mitosis. This strategy has paved the way for development of a new generation of chemotypes which selectively interfere with the target proteins. The interference of these anti-mitotic targets implicated in diverse stages of mitotic cell cycle progression culminates in cancer cell apoptosis.

AREAS COVERED

This review covers the various mitotic inhibitors developed against validated mitotic checkpoint protein targets using structure-based design and optimization strategies. The protein-ligand interactions and the insights gained from these studies, culminating in the development of more potent and selective inhibitors, have been presented.

EXPERT OPINION

The advent of structure-based drug design coupled with advances in X-ray crystallography has revolutionized the discovery of candidate lead molecules. The structural insights gleaned from the co-complex protein-drug interactions have provided a new dimension in the design of anti-mitotic molecules to develop drugs with a higher selectivity and specificity profile. Targeting non-catalytic domains has provided an alternate approach to address cross-reactivity and broad selectivity among kinase inhibitors. The elucidation of structures of emerging mitotic drug targets has opened avenues for the design of inhibitors that target cancer.

De Novo Design at the Edge of Chaos

Computational medicinal chemistry offers viable strategies for finding, characterizing, and optimizing innovative pharmacologically active compounds. Technological advances in both computer hardware and software as well as biological chemistry have enabled a renaissance of computer-assisted "de novo" design of molecules with desired pharmacological properties. Here, we present our current perspective on the concept of automated molecule generation by highlighting chemocentric methods that may capture druglike chemical space, consider ligand promiscuity for hit and lead finding, and provide fresh ideas for the rational design of customized screening of compound libraries.

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 de novo 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 de novo design and binding-kinetics-guided optimization for the efficient discovery of pioneering lead structures and prototyping drug-like chemical probes with tailored bioactivity.

Repurposing de novo designed entities reveals phosphodiesterase 3B and cathepsin L modulators

Scaffold hopping: a computational algorithm correctly predicted the macromolecular target ofde novogenerated small molecular entities.

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.

Future De Novo Drug Design

The computer-assisted generation of new chemical entities (NCEs) has matured into solid technology supporting early drug discovery. Both ligand- and receptor-based methods are increasingly used for designing small lead- and druglike molecules with anticipated multi-target activities. Advanced "polypharmacology" prediction tools are essential pillars of these endeavors. In addition, it has been realized that iterative design-synthesis-test cycles facilitate the rapid identification of NCEs with the desired activity profile. Lab-on-a-chip platforms integrating synthesis, analytics and bioactivity determination and controlled by adaptive, chemistry-driven de novo design software will play an important role for future drug discovery.

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

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.

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.