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

The Advent of Generative Chemistry

Generative adversarial networks (GANs), first published in 2014, are among the most important concepts in modern artificial intelligence (AI). Bridging deep learning and game theory, GANs are used to generate or "imagine" new objects with desired properties. Since 2016, multiple GANs with reinforcement learning (RL) have been successfully applied in pharmacology for de novo molecular design. Those techniques aim at a more efficient use of the data and a better exploration of the chemical space. We review recent advances for the generation of novel molecules with desired properties with a focus on the applications of GANs, RL, and related techniques. We also discuss the current limitations and challenges in the new growing field of generative chemistry.

Harnessing the potential of natural products in drug discovery from a cheminformatics vantage point

Natural products (NPs) present a privileged source of inspiration for chemical probe and drug design. Despite the biological pre-validation of the underlying molecular architectures and their relevance in drug discovery, the poor accessibility to NPs, complexity of the synthetic routes and scarce knowledge of their macromolecular counterparts in phenotypic screens still hinder their broader exploration. Cheminformatics algorithms now provide a powerful means of circumventing the abovementioned challenges and unlocking the full potential of NPs in a drug discovery context. Herein, I discuss recent advances in the computer-assisted design of NP mimics and how artificial intelligence may accelerate future NP-inspired molecular medicine.

Automating drug discovery

Small-molecule drug discovery can be viewed as a challenging multidimensional problem in which various characteristics of compounds - including efficacy, pharmacokinetics and safety - need to be optimized in parallel to provide drug candidates. Recent advances in areas such as microfluidics-assisted chemical synthesis and biological testing, as well as artificial intelligence systems that improve a design hypothesis through feedback analysis, are now providing a basis for the introduction of greater automation into aspects of this process. This could potentially accelerate time frames for compound discovery and optimization and enable more effective searches of chemical space. However, such approaches also raise considerable conceptual, technical and organizational challenges, as well as scepticism about the current hype around them. This article aims to identify the approaches and technologies that could be implemented robustly by medicinal chemists in the near future and to critically analyse the opportunities and challenges for their more widespread application.

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.

[In Silico Drug Design Using an Evolutionary Algorithm and Compound Database]

  Computational drug design plays an important role in the discovery of new drugs. Recently, we proposed an algorithm for designing new drug-like molecules utilizing the structure of a known active molecule. To design molecules, three types of fragments (ring, linker, and side-chain fragments) were defined as building blocks, and a fragment library was prepared from molecules listed in G protein-coupled receptor (GPCR)-SARfari database. An evolutionary algorithm which executes evolutionary operations, such as crossover, mutation, and selection, was implemented to evolve the molecules. As a case study, some GPCRs were selected for computational experiments in which we tried to design ligands from simple seed fragments using the Tanimoto coefficient as a fitness function. The results showed that the algorithm could be used successfully to design new molecules with structural similarity, scaffold variety, and chemical validity. In addition, a docking study revealed that these designed molecules also exhibited shape complementarity with the binding site of the target protein. Therefore, this is expected to become a powerful tool for designing new drug-like molecules in drug discovery projects.

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.

Merging allosteric and active site binding motifs: de novo generation of target selectivity and potency via natural-product-derived fragments

The de novo design of molecules from scratch with tailored biological activity is still the major intellectual challenge in chemical biology and drug discovery. Herein we validate natural-product-derived fragments (NPDFs) as excellent molecular seeds for the targeted de novo discovery of lead structures for the modulation of therapeutically relevant proteins. The application of this de novo approach delivered, in synergy with the combination of allosteric and active site binding motifs, highly selective and ligand-efficient non-zinc-binding (3: 4-{[5-(2-{[(3-methoxyphenyl)methyl]carbamoyl}eth-1-yn-1-yl)-2,4-dioxo-1,2,3,4-tetrahydropyrimidin-1-yl]methyl}benzoic acid) as well as zinc-binding (4: 4-({5-[2-({[3-(3-carboxypropoxy)phenyl]methyl}carbamoyl)eth-1-yn-1-yl]-2,4-dioxo-1,2,3,4-tetrahydropyrimidin-1-yl}methyl)benzoic acid) uracil-based MMP-13 inhibitors presenting IC₅₀ values of 11 nm (3: LE=0.35) and 6 nm (4: LE=0.31).

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.