Development of a New De Novo Design Algorithm for Exploring Chemical Space

Abstract

In the first stage of development of new drugs, various lead compounds with high activity are required. To design such compounds, we focus on chemical space defined by structural descriptors. New compounds close to areas where highly active compounds exist will show the same degree of activity. We have developed a new de novo design system to search a target area in chemical space. First, highly active compounds are manually selected as initial seeds. Then, the seeds are entered into our system, and structures slightly different from the seeds are generated and pooled. Next, seeds are selected from the new structure pool based on the distance from target coordinates on the map. To test the algorithm, we used two datasets of ligand binding affinity and showed that the proposed generator could produce diverse virtual compounds that had high activity in docking simulations.

Progress in visual representations of chemical space

“…Newer applications include the analysis of property distribution and landscapes in the datasets [60], target and pharmacophore mapping of the natural product space [63], prediction of melting points for ionic liquids [64], classification of drugs according to the solubility and metabolism [65]. Nevertheless, in certain cases no significant difference can be found between the SOM and GTM [66].…”

Inverse‐QSPR for de novo Design: A Review

“…Mishima et al . implemented the de novo Design Algorithm for Exploring Chemical Space (DAECS) which generates molecules on a specific 2D‐map region.…”

“…Molecules were extracted and the algorithm generated automatically similar structures. Extracted structures were modified by small changes on atoms or functional groups – i. e . addition, deletion, mutation of atoms, groups or bonds; (de)cyclization.…”

Finding Chemical Structures Corresponding to a Set of Coordinates in Chemical Descriptor Space

“…Another possible way to define TDDs was to make use of subspace and low dimensional manifold produced by such dimensionality reduction techniques as PCA and generative topographic mapping (GTM). [48] Like Mishima et al [49] and Takeda et al [50] succeeded to propose novel chemical structures corresponding to a target area on the GTM manifold, taking TDDs into account through subspace might give a better chance to find de novo chemical structures.…”