Generative Enriched Sequential Learning (ESL) Approach for Molecular Design via Augmented Domain Knowledge

TL;DR

This paper introduces an enriched sequential learning (ESL) approach that incorporates domain knowledge, such as drug-likeness scores, into generative models to improve the design of novel molecules with desirable properties.

Contribution

The paper proposes a novel ESL method that integrates domain knowledge into sequential learning models, enhancing their ability to generate molecules with targeted characteristics.

Findings

ESL improves the quality of generated molecules with higher QED scores.

Incorporating domain knowledge reduces bias towards prevalent but less desirable molecules.

The approach demonstrates better learning of specific chemical patterns.

Abstract

Deploying generative machine learning techniques to generate novel chemical structures based on molecular fingerprint representation has been well established in molecular design. Typically, sequential learning (SL) schemes such as hidden Markov models (HMM) and, more recently, in the sequential deep learning context, recurrent neural network (RNN) and long short-term memory (LSTM) were used extensively as generative models to discover unprecedented molecules. To this end, emission probability between two states of atoms plays a central role without considering specific chemical or physical properties. Lack of supervised domain knowledge can mislead the learning procedure to be relatively biased to the prevalent molecules observed in the training data that are not necessarily of interest. We alleviated this drawback by augmenting the training data with domain knowledge, e.g. quantitative estimates of the drug-likeness score (QEDs). As such, our experiments demonstrated that with this subtle trick called enriched sequential learning (ESL), specific patterns of particular interest can be learnt better, which led to generating de novo molecules with ameliorated QEDs.