Use case #1: AI in the pharmaceutical industry
Artificial Intelligence (AI) technology is rapidly advancing and becoming more integrated into various industries and applications, including the pharmaceutical industry. AI technologies such as natural language processing, computer vision, and time series analysis are making significant progress. It has the ability to process extensive data and detect patterns or trends invisible to humans. Which makes it a strong prospect for improving Drug discovery.
Drug discovery is a complex and time-consuming process. It involves identifying a target protein or disease and searching for compounds that can bind to that target and have a therapeutic effect or reduce the disease’s symptoms. This process involves testing a large set of molecules to find potential candidates and typically takes years or even decades. Which can be incredibly costly. However, AI has the potential to significantly speed up and streamline this process. In this case study, we will explore three ways AI can assist in drug discovery:
- by assisting in the generation of new molecules
- predicting their properties
- predicting reactions
Before that, for AI to be effective, we must have a clean and reliable data management pipeline. It ensures high quality, accurate and relevant data is used for AI analysis and drug discovery process. A clean data pipeline can cut down the time and effort of data analysis and ensure reliable lab results.
For example, one of our customers was able to cut manual analysis time from a week with a full-time analyst to a 5-minute automated analysis. Many organizations can attain this ROI and the resulting clean, reliable AI-ready data is a bonus.
AI for Molecule Generation:
One way AI can assist in drug discovery is by generating new molecules through a process called de novo design. It involves using machine learning algorithms to generate molecules that fit a set of properties constraints before it is synthesized. It significantly speeds up the process of identifying potential drug candidates, as it reduces the need for time-consuming and expensive synthesis and testing. Additionally, AI can optimize existing molecules by predicting how changes to their structure might affect their properties.
AI for Property Prediction:
Property prediction is a key area where AI can assist in drug discovery. It involves using machine learning algorithms to predict the properties of a molecule based on its structure. It includes predicting a molecule’s binding affinity or specificity for a target protein, or its solubility, bioavailability, toxicity, and potential off-target effects. By predicting these properties, we can identify molecules that have the desired characteristics for a drug candidate, and avoid those that may have undesirable properties. Thus greatly reducing the number of molecules that labs have to test.
For example, it could predict the binding affinity of a molecule for a target protein associated with a certain disease. Identifying molecules with a high binding affinity for the target protein can lead to drug candidates effective in treating the disease. Additionally, property prediction algorithms can predict the solubility of a molecule, which is important for determining its bioavailability and effectiveness as a drug.
At SquareFactory, we experimented with public datasets of molecules to train deep neural network models. We trained them to predict the toxicity of a molecule from its structure. We used a small dataset of molecules associated with their toxicity data and achieved good predictions of the toxicity of new molecules. It’s a promising AI development for the pharmaceutical industry that would allow companies to identify potentially toxic molecules early in the drug development process. Avoiding costly resource investment in their development.
AI for Reaction Prediction:
Reaction prediction is another way that AI can assist in drug discovery. It involves predicting the outcome of chemical reactions, to identify the best conditions for the synthesis of a molecule at a small or large scale. Additionally, reaction prediction can be used to identify potential synthetic pathways for a molecule, which can be important in the early stages of drug development.
Business Value, Outcomes, and Perspectives of AI in the Pharmaceutical Industry:
In conclusion, the application of AI in drug discovery holds great promise for transforming the way we identify and develop new drugs. AI has the potential to accelerate the drug discovery process by streamlining various aspects of drug development. We saw how AI can assist in generating new molecules with similar properties as target molecules. How it can predict the properties of molecules and choose the right ones to test. And how it can predict the outcomes of chemical reactions to discover new methods of synthesizing target compounds.
These are only a few examples of how AI can help in the complex process of drug discovery. As AI algorithms continue to advance in this field, we can expect to see a significant reduction in the time, resources, and costs associated with discovering new drugs.
In short, AI has the capacity to revolutionize the pharmaceutical industry, leading to faster and more efficient drug development. Pharmaceutical and biotech companies that are involved in any step of the drug discovery process could greatly benefit from integrating an AI strategy.
However, for AI to be truly effective, a clean and reliable data management pipeline must be in place. And as we’ve seen this first step can already bring a tremendous return on investments for companies.
SquareFactory is an enterprise AI-as-a-Service (AIaaS) company whose purpose is to make AI available and affordable to any company globally, in a sustainable way. We achieve this by providing highly specialized AI services and technology solutions that enable businesses to create and implement data-driven strategies, automate data analysis, and safely leverage artificial intelligence, at low costs.
Our main product, iSquare, is an end-to-end, automated MLOps platform, that enables companies to train, deploy and monitor machine learning models, at scale. iSquare is a framework-agnostic, modular platform that allows companies to boost their AI performance without the massive up-front investment in talent, systems, and computing resources.
SquareFactory assists companies in all of the different phases and aspects of their AI and Data projects, by providing highly specialized consulting, development, and research services.
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