Markus Hartenfeller

De novo Drug Design

Computer-assisted molecular design supports drug discovery by suggesting novel chemotypes and compound modifications for lead structure optimization. While the aspect of synthetic feasibility of the automatically designed compounds has been neglected for a long time, we are currently witnessing an increased interest in this topic. Here, we review state-of-the-art software for de novo drug design with a special emphasis on fragment-based techniques that generate druglike, synthetically accessible compounds. The importance of scoring functions that can be used to predict compound reactivity and potency is highlighted, and several promising solutions are discussed. Recent practical validation studies are presented that have already demonstrated that rule-based fragment assembly can result in novel synthesizable compounds with druglike properties and a desired biological activity.

Voyages to the (un)known: adaptive design of bioactive compounds

De novo drug design has emerged as a valuable concept for the rapid identification of lead structure candidates. In particular, fragment-based molecular assembly methods have been successfully employed for the automated design of screening compounds. Here, we review the current status of these approaches, with an emphasis on adaptive techniques that can be used to artificially evolve novel bioactive molecules. Evolutionary algorithms (EAs) and particle swarm optimization (PSO) are presented as preferred techniques for iterative virtual synthesis and testing. By the inclusion of straightforward synthesis rules, druglike compounds can be obtained. Evolving compound libraries are particularly suited for hit and lead finding in situations where resources are limited and the complete testing of a large screening compound collection is prohibitive.

A Collection of Robust Organic Synthesis Reactions for In Silico Molecule Design

A focused collection of organic synthesis reactions for computer-based molecule construction is presented. It is inspired by real-world chemistry and has been compiled in close collaboration with medicinal chemists to achieve high practical relevance. Virtual molecules assembled from existing starting material connected by these reactions are supposed to have an enhanced chance to be amenable to real chemical synthesis. About 50% of the reactions in the dataset are ring-forming reactions, which fosters the assembly of novel ring systems and innovative chemotypes. A comparison with a recent survey of the reactions used in early drug discovery revealed considerable overlaps with the collection presented here. The dataset is available encoded as computer-readable Reaction SMARTS expressions from the Supporting Information presented for this paper.

Concept of Combinatorial De Novo Design of Drug-like Molecules by Particle Swarm Optimization

We present a fast stochastic optimization algorithm for fragment‐based molecular de novo design (COLIBREE®, Combinatorial Library Breeding). The search strategy is based on a discrete version of particle swarm optimization. Molecules are represented by a scaffold, which remains constant during optimization, and variable linkers and side chains. Different linkers represent virtual chemical reactions. Side‐chain building blocks were obtained from pseudo‐retrosynthetic dissection of large compound databases. Here, ligand‐based design was performed using chemically advanced template search (CATS) topological pharmacophore similarity to reference ligands as fitness function. A weighting scheme was included for particle swarm optimization‐based molecular design, which permits the use of many reference ligands and allows for positive and negative design to be performed simultaneously. In a case study, the approach was applied to the de novo design of potential peroxisome proliferator‐activated receptor subtype‐selective agonists. The results demonstrate the ability of the technique to cope with large combinatorial chemistry spaces and its applicability to focused library design. The technique was able to perform exploitation of a known scheme and at the same time explorative search for novel ligands within the framework of a given molecular core structure. It thereby represents a practical solution for compound screening in the early hit and lead finding phase of a drug discovery project.

Enabling future drug discovery by de novo design

Computer‐assisted drug design is evolving as a source of innovation for drug discovery. In particular, de novo design approaches are being increasingly applied to find novel drug‐like compounds, molecular scaffolds, and bioisosteric replacements for established or unwanted fragments. Although some of the early software tools had a certain tendency to generate compounds of limited chemical attraction, modern de novo design algorithms put a strong emphasis on the synthesizability and drug‐likeness of machine‐generated compounds. We give an overview of the various methodologies for virtual compound construction, evaluation, and optimization in machina, and how they can support medicinal chemistry projects in the early phase of drug discovery.

Reaction-driven de novo design, synthesis and testing of potential type II kinase inhibitors

BACKGROUND De novo design of drug-like compounds with a desired pharmacological activity profile has become feasible through innovative computer algorithms. Fragment-based design and simulated chemical reactions allow for the rapid generation of candidate compounds as blueprints for organic synthesis. METHODS We used a combination of complementary virtual-screening tools for the analysis of de novo designed compounds that were generated with the aim to inhibit inactive polo-like kinase 1 (Plk1), a target for the development of cancer therapeutics. A homology model of the inactive state of Plk1 was constructed and the nucleotide binding pocket conformations in the DFG-in and DFG-out state were compared. The de novo-designed compounds were analyzed using pharmacophore matching, structure-activity landscape analysis, and automated ligand docking. One compound was synthesized and tested in vitro. RESULTS The majority of the designed compounds possess a generic architecture present in known kinase inhibitors. Predictions favor kinases as targets of these compounds but also suggest potential off-target effects. Several bioisosteric replacements were suggested, and de novo designed compounds were assessed by automated docking for potential binding preference toward the inactive (type II inhibitors) over the active conformation (type I inhibitors) of the kinase ATP binding site. One selected compound was successfully synthesized as suggested by the software. The de novo-designed compound exhibited inhibitory activity against inactive Plk1 in vitro, but did not show significant inhibition of active Plk1 and 38 other kinases tested. CONCLUSIONS Computer-based de novo design of screening candidates in combination with ligand- and receptor-based virtual screening generates motivated suggestions for focused library design in hit and lead discovery. Attractive, synthetically accessible compounds can be obtained together with predicted on- and off-target profiles and desired activities.

Drugs by Numbers: Reaction‐Driven De Novo Design of Potent and Selective Anticancer Leads

A potent and selective inhibitor of the anticancer target Polo-like kinase 1 was found by computer-based molecular design. This type II kinase inhibitor was synthesized as suggested by the design software DOGS and exhibited significant antiproliferative effects against HeLa cells without affecting nontransformed cells. The study provides a proof-of-concept for reaction-based de novo design as a leading tool for drug discovery.

‘Fuzziness’ in pharmacophore-based virtual screening and de novo design

Virtual screening technology permits rapid sieving through large screening compound collections and virtual compound libraries. It fuels hit identification projects by providing isofunctional bioactive compounds that are structurally unrelated to the original query. Such valuable starting points for lead discovery can be obtained by pharmacophore-based similarity searching, which has emerged as a preferred concept for retrieving novel bioactive chemotypes with minimal experimental effort. This review highlights ‘fuzzy’ pharmacophore concepts and applications in hit finding and molecular de novo design.

Concepts and applications of "natural computing" techniques in de novo drug and peptide design.

Evolutionary algorithms, particle swarm optimization, and ant colony optimization have emerged as robust optimization methods for molecular modeling and peptide design. Such algorithms mimic combinatorial molecule assembly by using molecular fragments as building-blocks for compound construction, and relying on adaptation and emergence of desired pharmacological properties in a population of virtual molecules. Nature-inspired algorithms might be particularly suited for bioisosteric replacement or scaffold-hopping from complex natural products to synthetically more easily accessible compounds that are amenable to optimization by medicinal chemistry. The theory and applications of selected nature-inspired algorithms for drug design are reviewed, together with practical applications and a discussion of their advantages and limitations.

Multistep virtual screening for rapid and efficient identification of non-nucleoside bacterial thymidine kinase inhibitors.

Antimicrobial activity of trimethoprim/sulfamethoxazole (SXT) against Staphylococcus aureus (S. aureus) is antagonized by thymidine, which is abundant in infected or inflamed human tissue. To restore the antimicrobial activity of SXT in the presence of thymidine, we screened for small-molecule inhibitors of S. aureus thymidine kinase with non-nucleoside scaffolds. We present the successful application of an adaptive virtual screening protocol for novel antibiotics using a combination of ligand- and structure-based approaches. Two consecutive rounds of virtual screening and in vitro testing were performed that resulted in several non-nucleoside hits. The most potent compound exhibits substantial antimicrobial activity against both methicillin-resistant S. aureus strain ATCC 700699 and nonresistant strain ATCC 29213, when combined with SXT in the presence of thymidine. This study demonstrates how virtual screening can be used to guide hit finding in antibacterial screening campaigns with minimal experimental effort.