Enhancing reaction-based de novo design using a multi-label reaction class recommender

Abstract

Reaction-based de novo design refers to the in-silico generation of novel chemical structures by combining reagents using structural transformations derived from known reactions. The driver for using reaction-based transformations is to increase the likelihood of the designed molecules being synthetically accessible. We have previously described a reaction-based de novo design method based on reaction vectors which are transformation rules that are encoded automatically from reaction databases. A limitation of reaction vectors is that they account for structural changes that occur at the core of a reaction only, and they do not consider the presence of competing functionalities that can compromise the reaction outcome. Here, we present the development of a Reaction Class Recommender to enhance the reaction vector framework. The recommender is intended to be used as a filter on the reaction vectors that are applied during de novo design to reduce the combinatorial explosion of in-silico molecules produced while limiting the generated structures to those which are most likely to be synthesisable. The recommender has been validated using an external data set extracted from the recent medicinal chemistry literature and in two simulated de novo design experiments. Results suggest that the use of the recommender drastically reduces the number of solutions explored by the algorithm while preserving the chance of finding relevant solutions and increasing the global synthetic accessibility of the designed molecules.

A New Hybrid Based on Long Short-Term Memory Network with Spotted Hyena Optimization Algorithm for Multi-Label Text Classification

“…The main goal of natural language processing in computer science is to create computational theories and empower systems to understand human language using machine learning algorithms and discover the relationships between documents [31]. MLTC is extensively used in a variety of applications such as document classification [32], web content classification [33,34], and recommendation systems [35,36]. MLTC is a complex task in NLP that requires the provisioning of multiple labels for a single instance.…”

Artificial intelligence in chemistry and drug design

“…Here, substantial progress has been made in all areas given both access to more experimental data [67,68] but also to the sophisticated techniques like Monte Carlo Tree Search (MCTS) which helps to identify the most likely synthetic routes in retro-synthesis planning using deep neural networks and symbolic AI [41]. In this special issue, Ghiandoni and colleagues present a novel reaction-based de novo design algorithm [69] adapting previously published work on reaction vectors [70,71] to optimise molecular structures that are likely to be more synthetically tractable. Using a recommender system, the authors demonstrate that their new methodology successfully prioritises the most relevant reaction vectors; this reduces the possibility of combinatorial explosion in the number of solutions while simultaneously ensuring that the probability of successful synthesis is high.…”

National Institutes of Health (NIH) Workshop on Reaction Informatics

“…Gillet's team has used two related machine learning models: a reaction classification model 137 and a reaction class recommender. 138 The former is a multiclass model which takes in a set of reaction vectors labeled by reaction class and predicts the reaction class of a previously unseen reaction. The recommender is a multilabel model which takes in a set of starting materials represented as molecular descriptors and labeled by reaction classes, and predicts a list of recommended reaction classes for a previously unseen starting material.…”

“…Avalon and FeatMorgan fingerprints gave the best performance followed by MACCS fingerprints. 138 Some results are shown in Figure 36.…”