CReM: chemically reasonable mutations framework for structure generation

Abstract

Structure generators are widely used in de novo design studies and their performance substantially influences an outcome. Approaches based on the deep learning models and conventional atom-based approaches may result in invalid structures and fail to address their synthetic feasibility issues. On the other hand, conventional reaction-based approaches result in synthetically feasible compounds but novelty and diversity of generated compounds may be limited. Fragment-based approaches can provide both better novelty and diversity of generated compounds but the issue of synthetic complexity of generated structure was not explicitly addressed before. Here we developed a new framework of fragment-based structure generation that, by design, results in the chemically valid structures and provides flexible control over diversity, novelty, synthetic complexity and chemotypes of generated compounds. The framework was implemented as an open-source Python module and can be used to create custom workflows for the exploration of chemical space.

EvoMol: a flexible and interpretable evolutionary algorithm for unbiased de novo molecular generation

“…The results can be found in Table 3. In addition to the GuacaMol baseline models, Graph GA [27] and SMILES LSTM [48], we compare ourselves to two recent methods, namely CReM [5] and MSO [9]. The three first columns for EvoMol correspond to the mean scores obtained on 10 runs for each initial conditions and parameters.…”

“…To limit the number of steps and to improve the likeliness of the solutions, they were commonly based on the combination of fragments rather than mutating the molecules at atomic level. The interest in evolutionary algorithms has decreased with the emergence of deep learning for molecular generation, although very recently, a new and efficient fragment based method was designed [5]. In the mid 2010s, Aspuru-Guzik and coll.…”

Assessing Methods and Obstacles in Chemical Space Exploration

“…(2) the assessment of candidate molecules, including determining to what degree (if any) a candidate satisfies the constraints of the objective(s) ( discrimination ); and (3) the search strategy, which integrates generation and discrimination to guide chemical space traversal ( exploration ). While several studies (Polishchuk, 2020;Hartenfeller & Schneider, 2011;Green et al, 2019;Segler et al, 2017) have made similar distinctions between components of a de novo drug design algorithm, no system adequately leverages the separation of these concerns in their implementation, specifically with respect to decoupling the chemical space search (exploration).…”

Assessing methods and obstacles in chemical space exploration

“…For any drug design algorithm, there are three main concerns: (a) the method(s) for generating chemically valid candidate molecule structures (generation); (b) the assessment of candidate molecules, including determining to what degree (if any) a candidate satisfies the constraints of the objective(s) (discrimination); and (c) the search strategy, which integrates generation and discrimination to guide chemical space traversal (exploration). While several studies 12,[14][15][16] have made similar distinctions between components of a de novo drug design algorithm, no system adequately leverages the separation of these concerns in their implementation, specifically with respect to decoupling the chemical space search (exploration).…”

“…Effectively traversing chemical space with traditional methods depends on the "step-size" of the methods being used to generate candidate molecules. The CReM framework for structure generation 15 describes these step-sizes with a distinction between atom-, reaction-, and fragment-based generators. An atom-based approach uses simple rules like addition, substitution, or deletion of bonds and atoms.…”