Power of AI: to identify better drug targets and drug candidates

by dr. Mahesh Bhalgat

The world has recently been taken over by OpenAI platforms, ChatGPT-3 and 4, which can mimic human intelligence. The pharmaceutical industry is also witnessing a revolution in the use of Artificial intelligence (AI) tools for drug discovery and development . Computational approaches have changed the early-stage drug discovery process from serendipitous discovery to rational discovery to intuitive discovery, making the process more streamlined, effective, and efficient. With the data available, AI and machine learning (ML) can better understand the steps, allowing scientists to move from data to decisions faster.

Traditionally, drug discovery has been a slow 10-15 year process, costing more than $2 billion to bring a new drug to market. Several factors lead to drug candidates being phased out during the drug discovery and development phase. As an example, undesirable pharmacokinetics and toxicity are important reasons for failed drug development. AI and data-driven R&D processes can help anticipate and eliminate some of these issues, resulting in a shorter drug discovery timeline, less development wear and tear, cheaper, and more efficient than ever.

Morgan Stanley research mentions that improvements in early-stage drug development using AI and ML over the next decade could result in 50 new therapies with a $50 billion market. This article will explore the benefits of AI tools and how they can transform drug discovery and development.

How AI helps identify better targets and candidates faster

AI helps identify better targets and candidates for drug development faster by using large data sets of biological and chemical information and applying machine learning algorithms to analyze the data. The AI can:

provide faster validation of the drug target
identify and design shock and lead compounds
optimize drug design

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AI models have helped identify potential cancer targets and the discovery of new drug candidates. These algorithms can identify drug candidates that are likely to have the desired pharmacological properties in terms of target specificity, potency, and pharmacokinetics, thereby reducing the number of drug candidates that need to be synthesized and tested in laboratories. Open databases such as UniProt, DrugBank, ChEMBL, and PubChem also play a critical role in drug discovery. These databases give researchers access to vast amounts of biological and chemical data, which can be used to recognize potential drug targets and predict drug efficacy. By making this data available to researchers around the world, open and public databases enable collaboration and accelerate drug discovery.

In recent years, in silico The technology has been widely used to assess the relevant properties of drugs in the preclinical stage and has produced many software programs and models, furthering the study of ADMET (absorption, distribution, metabolism, and excretion) in vitro (2)

Similarly, AI approaches such as deep learning and relevant modeling studies can be implemented for safety and efficacy assessments of drug molecules. Merck supported a computational model based on the quantitative structure-activity relationship (QSAR), which showed significant predictability for 15 drug candidate absorption, distribution, metabolism, excretion, and toxicity (ADMET) data sets.

Putting such tools together with good predictive power requires many elements to come together. High-quality data is at the heart of that along with a very good understanding of the underlying biology and a multidisciplinary team. Usually, the latter includes a group of scientists with experience around the world. Computational biology, computational chemistry, data science, and research informatics are needed for all research and development work. In the future, these tools may evolve in such a way that a team of chemists and biologists can perform desired AI/ML-based studies without relying on bioinformaticians.

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Pfizer is using AI to help revolutionize life-saving drug development. The company is advancing the biology of various diseases and using the knowledge to identify molecules that can potentially treat diseases. Atomwise has established a research collaboration with Sanofi that will leverage its AtomNet® platform for computational discovery and investigation of up to five drug targets. The AtomNet platform incorporates deep learning for structure-based drug design, enabling rapid, AI-driven searching of Atomwise’s proprietary library of over 3 trillion synthesizable compounds. Insilico Medicine, a clinical-stage drug discovery company, created the first AI-discovered anti-fibrotic drug and was the first in humans in February 2022. More recently, it has integrated AI chat functionality, GPT-chat, on its PandaOmics platform. This will allow researchers to have natural conversations with the platform, making it easier to navigate complex data and identify potential therapeutic targets and biomarkers. Syngene International, a leading scientific research, development and manufacturing company, has developed proprietary solutions – Syn.AI^TMfor AI-based drug discovery and Sarchitect^TM for QSAR modeling. These platforms help solve complex scientific problems in target identification, hit identification, lead optimization, and translational research. Both platforms are tuned for drug discovery in contrast to the generic tuneability provided by open source platforms.

Continued progress in AI technology is helping to accelerate the drug discovery process by enabling researchers to analyze and interpret vast amounts of data and identify better targets and drug candidates more quickly. Consequently, overcoming the challenges faced by pharmaceutical companies. This impact could be one reason behind the increasing number of biotech startups in this industry.

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The use of cutting-edge artificial intelligence technologies not only speeds product time to market, but also affects many aspects of the pharmaceutical value chain. Improve product quality, strengthen compliance, improve overall manufacturing process efficiency, make mistakes in operations. testing, aids in rapid troubleshooting, and is cost-effective

Dr. Mahesh Bhalgat, COO, Syngene International

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