Drug discovery is a notoriously long, complex and expensive process requiring the concerted efforts of the world’s brightest minds. The complexity in understanding human physiology and molecular mechanisms is increasing with every new research paper published and for every new compound tested. As the world is facing a new challenge in trying to both adapt to and defend itself against the coronavirus, artificial intelligence is offering new hope that a cure might be developed faster than ever before.
In this article, we will present some of the technologies being developed and applied in today’s drug discovery process, working side-by-side with scientists tracking new findings, and assisting in the creation of new compounds and potential vaccines. In addition, we will examine how the industry is applying AI in the fight against the coronavirus.
AI Applications for Drug Discovery
Start-ups focusing on the use of artificial intelligence in drug development and clinical trials have seen significant investment in recent years, and vendors focusing specifically on drug design and discovery received the majority of the total $5.2B funding observed between 2012 and 2019
Information Engines
Information Engines are fundamental machines behind applications in both drug discovery and clinical trials, serving as the basic information aggregator and synthesizer layer, on which the other applications can draw their insights, conclusions and prescriptive functions. The information available to scientists today is increasing exponentially, so the purpose of information engines being developed today is to help scientists update and aggregate all this information and pull out the data most likely to be relevant for a specific study.
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The types of information going into these engines vary broadly. An advanced information engine integrates information from multiple sources such as scientific research publications, medical records, doctors journals, biomedical information such as known drug targets, ligand information and disease-specific information, historical clinical trial data, patent information from molecules currently being investigated at global pharma companies, proprietary enterprise data from internal research studies at the individual pharma client, genomic sequencing data, radiology imaging data, cohort data and even other real-world evidence such as society and environmental data.
In a recent analyst insight, we discussed how these information engines are being applied in clinical trials to enhance success rates and reduce associated trial costs. When it comes to the upstream processes relating to drug discovery, their purpose is to synthesize and analyze these vast amounts of information to help the scientist understand disease mechanisms and select the most promising targets, drug candidates or biomarkers; or as we will see in the next section, to assist the drug design application in creating the molecular designs or optimize a compound with desired properties. Information is typically presented via a knowledge graph that visualizes the relationships between diseases, genes, drugs and other data points, which the researcher then uses for target identification, biomarker discovery or other research areas.
Drug Design
AI-based drug design applications are involved directly with the molecular structure of the drugs. They draw data and insights from information engines to help generate novel drug candidates, to validate or optimize drug candidates, or to repurpose existing drugs for new therapeutic areas.
For target identification, machine learning is used to predict potential disease targets, and an AI triage then typically orders targets based on chemical opportunity, safety and druggability and presents them ranked with most promising targets. This information is then fed into the drug design application which optimizes the compounds with desired properties before they are selected for synthesis. Experimental data from the selected compounds can then be fed back into the model to generate additional data for optimization.
For drug repurposing, existing drugs approved for specific therapeutic areas are compared against possible similar pathways and targets in alternative diseases, which creates an opportunity for additional revenue from already developed pharmaceuticals. It also gives potential relief for rare disease areas where developing a new compound wouldn’t be profitable. Additionally, keeping repurposing in mind during the development of a new drug as opposed to having a disease-specific mindset, may result in more profitable multi-purpose pharmaceuticals entering the market in the coming years.
Source: HIT Consultant