Abstract
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.
Highlights
De novo design aims at generating new chemical entities with drug-like properties and desired biological activities in a directed fashion [1,2]
De novo design is complementary to high-throughput screening in its approach to find innovative entry points for drug development [3]
Most of the approaches to de novo design attempt to mimic the work of a medicinal chemist: molecules are synthesized, tested for their biological activity, and the insight gained serves as the basis for the round of compound generation
Summary
De novo design aims at generating new chemical entities with drug-like properties and desired biological activities in a directed fashion [1,2]. Most of the approaches to de novo design attempt to mimic the work of a medicinal chemist: molecules are synthesized (virtually assembled from fragments), tested for their biological activity (computationally evaluated by a scoring function), and the insight gained serves as the basis for the round of compound generation (optimization). De novo design programs tackle this issue by employing rules to guide the assembly process. Such rules attempt to reflect chemical knowledge and thereby avoid the formation of implausible or unstable structures.
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