How AI is Helping Us Invent More Medicines?

How AI is Helping Us Invent More Medicines?

AI is now an integral component of the pharmaceutical industry, but the key question is whether AI will help humanity discover more medicines.

Artificial intelligence is more important than ever in the medical industry, according to several specialists in the field of medicine. People and the pharmaceutical business have suffered greatly from the lengthy studies and research required to develop and produce new drugs.

On the one hand, the pharmaceutical industry considers it challenging and exhausting; it simply takes too much effort and money to discover new medicines. On the other hand, if effective drugs for specific illnesses are not available at the right time, patients may face life-threatening consequences. Can machine learning and artificial intelligence aid in the discovery of novel drugs that have the potential to save many patients' lives?

AI in the medicine sector: What do we know so far?

The application of AI in medicine is a fascinating endeavor since, according to some, scientists now possess true superpowers that will enable them to save more lives. The media has already begun to pay attention to the impending change in the drug industry.

One of the major ways AI is revolutionizing the medicine industry is its capacity to speed up the drug discovery process. Traditionally, identifying prospective medication candidates has been a hard and time-consuming task that involved screening enormous libraries of chemical compounds.

However, with the introduction of machine learning algorithms, researchers can now examine huge information, discover trends, and identify potentially useful compounds with increased speed and precision.

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Case studies in medicines that involved artificial intelligence and machine learning

An important stage in the drug development process is predicting the binding affinity between tiny compounds and protein targets, and AI-powered firm Atomwise has done just that with a deep learning model.

Atomwise used AtomNet to search through a massive library of possible drug molecules to find ones that could work against 318 different disease targets. A target is usually a protein that causes or is involved in a disease. Traditional methods, called high-throughput screening (HTS), can’t handle the huge number of possible molecules out there, but AtomNet can.

On average, it discovered about seven unique molecules for each target, which is a lot better than traditional methods. Some of these targets were really tough to work with and had never been successfully targeted before, like proteins involved in Parkinson’s disease and certain types of cancer.

One of the coolest things about AtomNet is that it doesn’t need any prior information about the targets to make these discoveries. It can predict which molecules might work just by looking at the structure of the proteins.

Atomwise is now working on turning some of these discoveries into actual medicines. They even plan to start testing a new drug for treating diseases related to an enzyme called TYK2 later this year.

In another milestone accomplishment, Professor Andrew Hopkins, CEO of Exscientia, stated that the successful invention of DSP-1181 proves AI's ability to greatly improve drug discovery methods.

This chemical, developed in partnership by Exscientia and Sumitomo Dainippon Pharma, is designed to treat obsessive-compulsive disorder (OCD). DSP-1181's discovery and optimization took only 12 months, which is much shorter than traditional drug development processes.

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A Utah-based business called Recursion Pharmaceuticals has created a large-scale platform for finding and creating innovative medicines that is powered by artificial intelligence.

Recursion has joined Nvidia's BioNeMo platform, launching its Phenom-Beta program for widespread use. CEO Chris Gibson sees this collaboration as a significant step, comparable to the importance of genomics. Recursion aspires to significantly contribute to the AI-based drug discovery environment by exploiting a sophisticated internal operating system that converts cellular images into relevant research findings.

A few other startups are also at the forefront of integrating AI into drug discovery.

Here are some notable ones:

  • Insilico Medicine, based in Hong Kong, uses AI to find new medications and biomarkers for aging. Their AI platform combines genomes, transcriptomics, and proteomics data to find and optimize medication targets.

  • BenevolentAI, with its headquarters in the UK, uses AI to connect and exploit scientific data, providing relevant insights for medication research. Their artificial intelligence models aid in the identification of novel targets and the understanding of disease causes, which accelerates the development of new medicines.

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AI-based techniques involved in the invention of new medicines

  • Predictive retrosynthesis uses artificial intelligence to design potential synthesis paths for molecules. This technique assists scientists in identifying the optimum synthesis pathways, hence speeding up the drug discovery process. AI algorithms, particularly those based on deep learning, have demonstrated exceptional accuracy in predicting plausible synthetic paths.

  • Accurate prediction of protein structures is critical for understanding molecular interactions and medication discovery. AlphaFold's AI algorithms have achieved unparalleled accuracy in predicting protein folding, allowing for the development of novel therapeutics.

  • Property prediction is the process of applying artificial intelligence models to forecast the physical, chemical, and biological properties of molecules. Techniques like QSAR and QSPR are widely utilized, in which AI models learn from current data to predict the properties of new molecules, which ultimately aids in the selection of potential candidates for further research.

In short, AI appears to have a bright future in the pharmaceutical sector. The "AI in Pharma" trend is so prevalent that many experts believe that the widespread adoption of AI-based medications by healthcare institutions must accelerate with proper guidelines and rules.

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AI in finding new medicines: It’s a face-off that mankind wants

For centuries, developing new pharmaceuticals to treat human diseases has relied heavily on human intuition and painstaking testing. However, this is changing rapidly due to advancements in artificial intelligence.

The medical business is currently overflowing with new research and findings. In addition, startups operating in this field have demonstrated enormous commitment and business acumen in leading humanity's race to high-efficacy medications that can save lives.

If you have some more fascinating facts about the pharmaceutical industry, please share them in the comments section.

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Additional Resources

Drug discovery with AI at AstraZeneca

Discovering and designing drugs with AI

Evolution of AI in small molecule drug discovery

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