Building Accurate and Diverse Data Sets for Retrosynthetic Planning

Posted on : November 2nd 2021

Posted by : Viswanathan Chandrasekharan

Building Accurate and Diverse Data Sets for Retrosynthetic Planning

Retrosynthetic planning uses artificial intelligence (AI) and machine learning models. However, the data used to train the models determine the accuracy, uniformity, and reproducibility of the predictions. Therefore, high-quality and diverse training data sets to optimize key synthetic planning initiatives are needed for generating novel predictions. Traditionally, retrosynthetic analyses were carried out manually. Advancements in AI and specifically deep learning have spawned sophisticated and automatic algorithms with the potential to provide retrosynthetic analysis with broader applications and better accuracy. Retrosynthetic prediction tools leverage up-to-date research from across the globe for assisting chemists in designing synthetic routes to novel molecules and predicting outcomes. These tools have many applications in drug discovery, medicinal chemistry, materials science, and natural product synthesis analysis. However, the successful application of AI to chemical synthesis and prediction accuracy depends upon the quality and the diversity of the data. Therefore, to optimize the use of AI for retrosynthetic planning and increase the accuracy of predictions, the training sets should be enriched with high-quality, diverse reaction data.

Diverse and Accurate Data Drive AI-Enabled Retrosynthetic Planning Model

In retrosynthetic planning, the goal is to reduce the complexity of the molecular target, and it is achieved by creating diverse and accurate synthetic routes. However, AI and machine learning models used in retrosynthetic planning applications are only as good as the chemical structures and ten-thousands of data points sourced from multiple sources. The accuracy of predicting retrosynthetic pathways depends on the training data’s quality, diversity, and accuracy. Data diversity is a key challenge in data-driven automatic retrosynthetic route planning. If the training data do not encompass all chemical and chemistry subspaces, the results will be limited in scope and novelty. Therefore, improving the predictive power of synthesis planning requires a diverse range of reaction data. Hence, the chemical information accessible across patents, journals, and scientific publications from across the world should be first extracted. Subsequently, the data should be enriched, managed, and delivered as integratable data for retrosynthetic analysis. In addition, this process should be ongoing and continue in tandem with machine learning to empower and enrich AI retrosynthetic planning.

Optimize Your Outcomes with Straive Data Solutions

The challenges posed by the pandemic due to COVID-19 have accelerated digital transformation in the pharmaceutical and life sciences fields. The breakneck speed at which vaccines have been developed portends that the development of other therapeutics could also be fast tracked in the future.
In addition, the pandemic has added to the large number of research papers being published every year. As a result, when researchers and scientists compile data for meta-analysis, they find it challenging to evaluate and compare technical notes about a compound and its comparators. Straive’s data solutions suite is designed for extracting data from text, images, tables, and plots in patents, journals, and scientific publications. Powered by our proprietary AI-enabled Straive Data Platform, our unstructured-data solutions are capable of selective data picking from tables and numeric data farming from graphs, images, and figures. Our in-house chemistry subject matter experts validate the extracted data. Subsequently, the data are enriched, managed, and delivered as integratable data that can be used as training data sets or by scientists and researchers in real-life scenarios.

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