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

Abstract

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.

e-LEA3D: a computational-aided drug design web server

“…On the contrary, alternative approaches as de novo drug design use search strategies to efficiently explore the chemical space without a fully enumeration. Particle Swarm Optimization (4) or Evolutionary Algorithms (5), analogous to Genetic Algorithms (6–9), are examples of heuristics employed for this purpose. The main downside of this strategy is that it can generate compounds that are promising but difficult to synthesize (10).…”

Multi-objective de novo drug design with conditional graph generative model

“…Early works have developed various algorithms to produce new molecular structures, such as atom based elongation or fragment based combination [3,4]. Those algorithms are often coupled with global optimization techniques such as ant colony optimization [5,6], ge-Full list of author information is available at the end of the article netic algorithms [7,8] or particle swam optimization [9] for the generation of molecules with desired properties.…”

DOGS: Reaction-Driven de novo Design of Bioactive Compounds

“…Such rules attempt to reflect chemical knowledge and thereby avoid the formation of implausible or unstable structures. For example, some assembly approaches prevent connections between certain atom types, and finally the formation of unwanted substructures [6], [7]. Other strategies employ chemistry-driven retrosynthetic rules capturing general principles of reaction classes [2].…”