AI pharmaceuticals are maturing, and data sharing needs to be broken

Deeppotential has also successfully applied Uni-Mol to more fields, such as material design. "Recently, we significantly upgraded Uni-Mol's model framework with the launch of Uni-Mol+ and won the top spot in the international authoritative academic competition OGB-LSC's prediction of quantum chemical properties. In the future, it will help Deeppotential's Hermite® drug computing design platform calculate more accurately." Fan Mengqi said.

According to him, traditional CADD is often based on traditional molecular dynamics (MD) sampling to simulate dynamic processes, but it is difficult to capture complete conformational changes when calculating a large protein system. The RiDYMO™ Enhanced dynamics platform created by Deeppotential uses AI to capture protein dynamic changes and explore the complete conformation space, enabling protein functional studies and allosteric/allosteric systems to find drug candidate molecules.

For example, he said, the company has a self-developed pipeline for tumor therapy, using enhanced kinetic methods to find budding compounds, "this target is an inherently disordered protein (IDP), in the human body lacks a stable three-dimensional conformation, the traditional CADD method can not effectively describe this type of protein, let alone rational development from scratch."

"We do not emphasize how fast the research and development time is, but really focus on the project logic of the project itself, especially for some difficult drug targets with great potential but not yet ready drugs, and it is not time to catch up." When you see something that no one else has seen yet, it's also a good way to develop it to a certain stage and then look for cooperation."

In Zhong Wenge's view, "AIDD (AI Drug Discovery) plays a great role in many aspects of innovative drug research and development, but its full realization may take a long time."

"Structural biology is the 'gene' of Regal medicine." Zhong Wenge said that Rui Ge Pharmaceutical is positioned as an innovative pharmaceutical company with "roots in China and global layout", and chooses to walk on two legs. One is to use the self-developed rCARD™ platform to carry out complex computer modeling with the help of tools such as computational biology, structural biology and computational chemistry. The second is the direct analysis of protein structure. At present, Ruege has worked with the Shanghai Institute of Pharmacology of the Chinese Academy of Sciences and the industry's top structural biology teams such as Weiya and Shuimu Future to analyze nearly 100 protein complex structures, including 5 cryo-electron microscope structures.

"Our self-created rCARD™ platform has been validated at an internal technical level." Zhong Wenge introduced that through man-machine combination, dry and wet test combination, Ruge Medicine spent ten months to discover the highly active and highly selective candidate drug RGT-419B molecule, achieving a complex balance of selective activity in the development of CDK4/2/6 inhibitors. At present, the Phase I clinical trial of RGT-419B is in progress.

"Our attitude is to embrace technology and embrace the future." Zhong Wenge said that Ruge Pharmaceutical has made efforts in two aspects: one is to transform the passive auxiliary thinking of CADD into active thinking, and the molecular optimization design is guided by information and calculation; The second is to actively adopt existing useful AI algorithms and collaborate with world-class AI teams to develop new methods. "We don't just do technology for technology's sake. We do everything with the goal of developing innovative drugs."

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"There have been a number of large and small companies competing to lay out the AI track, such as mushroomed momentum, but ultimately did not deliver satisfactory results to the market, that is, there is not much difference with the existing CADD." Recall Tang Qiusong, head of Roche China accelerator.

"Now everything is crowned with AI, including cell therapy is also crowned with AI, I think it is a bit exaggerated." It is now said that AI can design antibodies, proteins, RNA and so on, in fact, I think the most successful is small molecule drug design." Chen Chunlin believes that after a hundred years of research and development, small molecular compounds have a large number of literature records and method accumulation, which can provide a solid foundation for AI learning. Therefore, the easiest area for AI to achieve breakthroughs is to analyze and synthesize small molecular compounds to predict the toxic effects of drugs into the human body.

Medisi is a CRO company that actively embraces AI technology, and according to the company's internal statistics, in different cases, adding AI can indeed improve the speed and effectiveness of drug design, thereby improving the efficiency of pharmaceutical research and development. However, Chen Chunlin is still rationally and cautiously optimistic about AI pharmaceuticals.