How does Life science move towards AI4S era?

New methods and new tools have opened up a new chapter in the field of life Science, and now the arrival of AI for Science is bringing unprecedented new opportunities to the field of life science. At the academic summit of 2023 Science Intelligence Summit "Al for Life Sciences" held on August 11, excellent experts and scholars at home and abroad shared their insights on key scientific issues of AI for Life Sciences. This paper discusses how life science moves towards AI4S era.

AI has become an important factor driving drug development

Zhang Ao, distinguished professor and dean of School of Pharmacy, Shanghai Jiao Tong University, believes that with the rapid development of life science technology and disease biology, the biomedical research paradigm and industrial development pattern are undergoing complex and profound changes. In particular, with the rapid evolution of advanced computing technology and artificial intelligence technology, the use of massive data and advanced methods to accelerate drug development and promote the evolution of computing resources to the natural stage has become a development trend, and the innovation of AI computing tools has become an important factor driving drug research and development.

Drug discovery is a multi-stage, lengthy and expensive process, and efficiency gains at each stage have enormous value. With the blessing of data-driven, "AI+ drug research and development" is becoming an inevitable development trend.

Professor Yang Shengyong of the State Key Laboratory of Biotherapeutics, West China Hospital, Sichuan University, has been engaged in computer-aided drug molecular design research for a long time. In recent years, he has carried out in-depth research in AI-based drug molecular design, especially in molecular generation models. For example, the team established a novel molecular generation model based on conditional recurrent neural networks and applied this model to the development of RIPK1 small molecule inhibitors, discovering a highly active and highly selective RIPK1 inhibitor with a novel structure, which demonstrated the ability of generative deep learning (GDL) models to generate novel molecular structures. It shows that deep learning has great potential in the field of drug discovery. The study was published in Nat Commun (2022).

"Our team introduced artificial intelligence algorithms into drug molecular design and established a new method of drug molecular design based on capsule neural network, using vector neurons instead of traditional scalar neurons, which can solve the small sample problem encountered in drug research and development to a certain extent, and the learning effect of data is better. Significantly improve the accuracy of drug molecular design and pharmacokinetic prediction. We have also recently developed molecular generation models based on conditional recurrent neural networks and autoregressive flows, which have demonstrated a good ability to generate novel structurally drug-like molecules." Yang Shengyong said.

AI is significantly empowering the overall drug discovery process. By using AI technologies such as deep learning and reinforcement learning, researchers can quickly screen potential drug molecules, predict drug targets, and optimize drug molecular design, greatly improving the efficiency and success rate of drug research and development.

How to make AI better serve drug development? Ouyang Defang, a professor at the Chinese Institute of Medicine, University of Macau, has been focusing on computational pharmacy research since 2011, establishing a drug preparation database and developing a machine learning algorithm for drug preparation prediction, thereby building an artificial intelligence platform for drug preparation, and combining quantum mechanics, molecular simulation and pharmacokinetic simulation for drug dosage form research.

Ouyang Defang said that modern pharmacy from "physical pharmacy", "nanomedicine" to "computational pharmacy", the field of pharmacy has made great progress in the past decade, but there are still many challenges. The first is the lack of high-quality data, the current pharmaceutical factory is still a data island, how to share data is very important. The second is that there is still a lack of user-friendly digital tools or computing tools for prescription developers, and there is an urgent need to develop user-friendly computing tools. The third is interdisciplinary personnel training, high-quality personnel is the fundamental to promote the development of this field.

The rapid development of machine learning based on data modeling and innovative intelligent computing tools will continue to meet the increasingly diverse analysis needs and promote the transformation of the life sciences industry. In general, the paradigm of drug discovery is undergoing tremendous disruption and change.

AI4S new paradigm injects new vitality into life science research

Artificial intelligence is playing an increasingly important role in the study of biological mechanisms, the screening, detection and treatment of diseases. With the completion of the Human Genome Project, scientists have a better understanding of the relationship between disease and genes.