DOGS: Reaction-Driven de novo Design of Bioactive Compounds

We present a computational method for the reaction-based de novo design of drug-like molecules. The software DOGS (Design of Genuine Structures) features a ligand-based strategy for automated ‘in silico’ assembly of potentially novel bioactive compounds. The quality of the designed compounds is assessed by a graph kernel method measuring their similarity to known bioactive reference ligands in terms of structural and pharmacophoric features. We implemented a deterministic compound construction procedure that explicitly considers compound synthesizability, based on a compilation of 25'144 readily available synthetic building blocks and 58 established reaction principles. This enables the software to suggest a synthesis route for each designed compound. Two prospective case studies are presented together with details on the algorithm and its implementation. De novo designed ligand candidates for the human histamine H4 receptor and γ-secretase were synthesized as suggested by the software. The computational approach proved to be suitable for scaffold-hopping from known ligands to novel chemotypes, and for generating bioactive molecules with drug-like properties

IADE: a system for intelligent automatic design of bioisosteric analogs

Abstract IADE, a software system supporting molecular modellers through the automatic design of non-classical bio-isosteric analogs, scaffold hopping and fragment growing, is presented. The program combines sophisticated cheminfor-matics functionalities for constructing novel analogs and fil-tering them based on their drug-likeness and synthetic accessibility using automatic structure-based design capabil-ities: the best candidates are selected according to their simi-larity to the template ligand and to their interactions with the protein binding site. IADE works in an iterative manner, improving the fitness of designed molecules in every gener-ation until structures with optimal properties are identified. The program frees molecular modellers from routine, repeti-tive tasks, allowing them to focus on analysis and evaluation of the automatically designed analogs, considerably enhanc-ing their work efficiency as well as the area of chemical space that can be covered. The performance of IADE is illustrated through a case study of the design of a nonclassical bioisos-teric analog of a farnesyltransferase inhibitor—an analog that has won a recent ‘‘Design a Molecule’ ’ competition