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New Technology to Promote Drug Development-AI Technology

New Technology to Promote Drug Development-AI Technology

For a long time, the research and development of each new drug has faced the challenges of high cost and long cycle. In response to these challenges, major pharmaceutical companies have shifted from targeting common diseases to developing drugs for specific diseases. At the same time, these companies are constantly looking for new technologies for new drug development, such as high-throughput screening, DNA encoded chemical library, computer-aided drug discovery, and artificial intelligence.

The use of computer simulation for drug development may reduce costs and increase the efficiency and success rate of the drug development process. Since the 1990s, computer methods such as homology modeling, molecular docking, quantitative structure-activity relationship and molecular dynamics simulation have been used for drug development. But thanks to the rapid growth of computing power and the rise of large data sets, artificial intelligence (AI) technology has begun to be applied to enhance the process of drug development. The use of AI to develop new drugs is to simulate the process of drug screening by computer to predict the activity of compounds, and then conduct targeted physical screening of potential compounds.

The main process of drug discovery The main process of drug discovery

Machine learning (ML)

The method of using AI in the discovery of small drug-like molecules involves the use of chemical space. By enumerating possible organic molecules by calculation, the chemical space provides a stage for the identification of brand-new high-quality molecules. Machine learning is a type of technology in artificial intelligence, which includes supervised learning, unsupervised learning and reinforcement learning. Machine learning technology helps to identify target-specific virtual molecules and their associations with their respective targets, while optimizing safety and efficacy attributes. QSAR/QSPR models are currently widely used. The applications of machine learning in drug development include but are not limited to the following: biological activity or physicochemical prediction, prediction of drug-protein and drug-drug pair interactions, and de novo molecular design with ideal pharmacological properties The molecular structure, synthesis accessibility prediction, prediction of the product of the synthesis reaction.

Deep learning (DL)

Deep learning is one of the latest technologies in the field of machine learning, which is based on the development of artificial neural networks in machine learning. This technology has shown superior performance to other machine learning algorithms in fields such as image and speech recognition, natural language processing, etc. Deep learning can process a large amount of data that has not been deeply processed manually, and automatically extract features through continuous learning for characterization simulation and classification.

AI technology in small molecule drug development AI technology in small molecule drug development

Neural network and deep learning network architecture Neural network and deep learning network architecture

Deep learning architecture for small molecule drug discovery

There are different types of DL architectures, each of which can recognize patterns and extract high-level features in different ways based on the structure of the training data.

Machine learning has proven to be a useful tool in the field of drug discovery. Compared with other methods, deep learning has a more flexible architecture, so it can be dedicated to solving a certain type of problem. These computational tools have shown outstanding performance in target identification, compound screening, de novo drug design and clinical prediction. With the advancement of artificial intelligence technology, the impact of AI on drug development will only increase.

Driving innovation in drug development with AI technology

BOC Sciences integrates advanced AI algorithms into drug discovery to enhance decision-making and accelerate timelines. Our expertise ensures data-driven, efficient development strategies.

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Reference

  1. Chen H.M., Engkvist O., et al. The rise of deep learning in drug discovery. Drug Discovery Today. 2018, 23: 1241-1250.

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  • Advanced computer aided technology
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