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Four Major Benefits of AI in Drug Discovery


Among various applications of AI technology in the pharmaceutical industry, some are viewed as most important and worth more depth of exploration.
  • Author Name: Lisa George
Editor: Lisa George Last Updated: 03-Aug-2021

Among various applications of AI technology in the pharmaceutical industry, some are viewed as most important and worth more depth of exploration.

AI assists in discovery of intervention targets

The first step in drug development is to understand the biological origin and mechanism of the disease, and then to determine suitable targets through high-throughput technologies such as shRNA screening and deep sequencing, and finally to find relevant patterns through a large number of diverse data sources. This is huge work and often presents an important challenge for traditional methods.

Unlike traditional methods, AI can systematically analyze existing literature and data in just a few seconds. This real-time "omics" database analysis can more accurately understand pathological cells and molecular mechanisms, and it can be used for complex diseases such as neurodegenerative diseases.

In order to find intervention targets that may play a role in the disease, the biopharmaceutical company BERG uses AI to screen information from human tissue samples, that is, extract genomics, proteomics, metabolomics and other data from the patient’s tissue samples, and then deep learning is performed to search for the difference between non-disease and disease states, and find proteins that have an impact on the disease.

Another company BenevolentAI’s JACS (Judgment Augmented Cognition System) technology platform uses AI to extract knowledge that can promote drug research and development from the disorganized mass of information, and put forward new hypotheses that can be verified, thereby accelerating drugs research and development process.

AI assists in screening candidate drugs for small molecule libraries

After determining the target of intervention, it is necessary to find a compound that can interact with the target in an ideal way. This process includes screening thousands of potential natural, synthetic or bioengineered compounds and understanding their impact on the target. AI can quickly predict drug candidates from millions of small molecules that can effectively bind to the target and have minimal side effects based on structure and interaction modeling calculations.

As the first new drug discovery platform based on the structure of small molecules, Atomwise's AtomNet platform can learn the three-dimensional characteristics of the combination of small molecules and target targets for the discovery and optimization of lead compounds, and within a few days new compounds are discovered. Atomwise has cooperated with global pharmaceutical companies and 200 universities and hospitals in 40 countries or regions to implement hundreds of projects each year.

Another method is called network-driven new drug discovery. Through large proprietary databases and tailor-made computing tools, it is not just predicting the combination of a drug and a target molecule, but the impact to the entire disease-related signal pathway network. For example, e-therapeutics and Novo Nordisk are using the Internet to drive the search for new treatments for type 2 diabetes.

AI assists in repurposing existing drugs

AI can better understand drug pharmacology and can be used for drug optimization and exploring new use of old drugs by determining off-target reactions and toxicity.

Cyclica is the first cloud-based proteomics platform (Ligand Express), which screens all protein targets that can bind to small molecule compounds, and uses AI to evaluate the impact of these compounds on protein targets, and uses bioinformatics to present the interactive relationship between drugs and proteins as images. This platform enables a unique and comprehensive evaluation of small molecule compounds. This information can help improve drug efficacy, prevent drug side effects, and discover new targets that bind to small molecule compounds.

AI assists in new drug design

New drug design can achieve precise customized design, avoiding bias and unnecessary cross-offset in small molecule screening. Insilico Medicine announced the development of GENTRL in September 2019, and designed six new molecules within 21 days (costing $150,000), four of which can be specifically inhibited DDR1 at nanomolar concentrations.

At the end of January 2020, Exscientia announced that DSP-1181 jointly developed with Japan's Sumitomo Pharmaceuticals has entered the first clinical phase for patients with obsessive-compulsive disorder. This is the first time that a drug molecule designed entirely by AI has entered human clinical trials, and the entire development time is only twelve months, marking a crucial milestone in the discovery of new AI drugs.

Protheragen MedAI is an AI-driven drug R&D company that has successively launched a number of drug discovery prototypes, from the early development stage (AI-driven drug synthesis, drug design, drug activity prediction) to the clinical research stage (AI-driven pharmacovigilance system, registration transaction system, clinical data programming system) and so on, covering a series of key nodes in the whole process of new drug research and development. Meanwhile, it also offers comprehensive solutions for medical Imaging and medical therapy and research systems.