Posted on : November 2nd 2021
Retrosynthetic analysis and planning is a widely used technique in chemical synthesis that helps deconstruct a target chemical compound progressively into simpler compounds by known methods and supports the planning of a final synthesis route to the target, based on cost, efficiency, and other parameters.
Typically, retrosynthetic analyses are carried out manually. Advancements in artificial intelligence (AI) and specifically deep learning have spawned sophisticated and automated algorithms with the potential to provide retrosynthetic analysis with broader applications and better accuracy. Retrosynthetic prediction tools can leverage up-to-date research from across the globe for assisting chemists in designing synthetic routes to novel molecules. These tools have many applications in drug discovery, medicinal chemistry, materials science, and natural product synthesis.
AI-enabled retrosynthetic planning – a roadmap to guide the synthesis of a molecular target – uses machine learning (ML) models to achieve the required results. However, the data used to train the models determine the accuracy, uniformity, and reproducibility of the predictions. Therefore, high-quality and diverse training reaction data sets are needed to optimize automated synthetic planning initiatives.
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, machine-learning models used in retrosynthetic planning applications are only as good as the chemical structures and thousands of data points sourced from multiple sources. 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 efficiency.
For improving the predictive power of AI-enabled retrosynthesis planning, a very large corpus of chemical information accessible across patents, journals, and other scientific publications must be first curated. Subsequently, the data should be enriched, managed, and presented 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-supported retrosynthetic planning.
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. Using a reliable synthesis plan from AI-driven retrosynthetic planning for the quicker and successful synthesis of target molecules could support and fast-track therapeutics development.
Straive’s data solutions suite is designed for extracting data from text, images, tables, and plots in patents, journals, and scientific publications of different formats.
Powered by our proprietary AI-enabled Straive Data Platform, our unstructured-data solutions are capable of selective data picking from tables and numerical data farming from graphs, images, and figures. Data thus extracted are enriched and validated by our in-house chemistry subject matter experts. Subsequently, the data is delivered as integratable data that can be used as training data sets for retrosynthetic planning initiatives or used by scientists and researchers to gain further insights.
The process of data extraction involves identifying and recovering alternative and semi-structured data from various data sources such as files, XMLs, JSON, etc.
Capital markets are an excellent example of a perfect competition. The nature of the market is such the participants have to be competitive and result focussed. For instance, brokerages and investment banks have to deliver passive gains for their clients and, at the same time, earn a margin for themselves.
Today’s ESG analytics require processing data, patterns, and hidden connections to provide insights that investors, asset managers, and companies need. For example, Straive deploys advanced machine learning algorithms to analyze reams of documents to collect evidence across executive statements for signs of vagueness or obfuscation.
Talking about using data to gain insights is easy. But actually doing it will uncover a newer set of challenges, especially when it comes to unstructured data.
Integrating ESG data into commodities trading operations requires structured, easy-to-consume data. By their nature, ESG data resist such integration, and highly scalable data solutions across the data life cycle are needed to allow stakeholders to deploy end-to-end data solutions for a successful data-to-intelligence journey.
Our solutioning team is eager to know about your challenge and how we can help.