
==== Front
Innovation (Camb)
Innovation (Camb)
The Innovation
2666-6758
Elsevier

S2666-6758(24)00123-1
10.1016/j.xinn.2024.100685
100685
Commentary
Drug development in the AI era: AlphaFold 3 is coming!
Shi Yi shiyi@im.ac.cn
1234∗
1 CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences (CAS), Beijing 100101, China
2 Medical School, University of Chinese Academy of Sciences, Beijing 100049, China
3 Beijing Life Science Academy, Beijing 102209, China
4 Health Science Center, Ningbo University, Ningbo 315211, China
∗ Corresponding author shiyi@im.ac.cn
14 8 2024
09 9 2024
14 8 2024
5 5 10068515 5 2024
10 8 2024
© 2024 The Author(s)
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Published Online: August 14, 2024
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pmcMain text

For decades, researchers have sought a convenient, reliable, and fast track to drug development. Usually, it takes about 12–15 years from the initial drug discovery stage to the point at which the drug gets approval by national drug administration agencies. Moreover, when we consider the costs of both successful and failed programs, it costs about 2.5 billion US dollars to bring a drug to market (Figure 1). How to reduce the time and costs required for drug development is a subject that we are always thinking deeply about.Figure 1 Comparison of characteristics of conventional drug development and AI tool-accelerated drug discovery

Top, it will take about 12–15 years and cost about 2.5 billion US dollars from initial drug discovery to market in the conventional drug development way. Bottom, it will significantly decrease the investment and time required to develop a novel drug with the help of AI tools.

Generally, drug development will undergo several specific steps. It often starts with the discovery stage, when we identify the biological target responsible for a disease, possibly a protein receptor or an enzyme, and then perform screening for molecules that might interact with the target. Once we get the resulting candidates, we further work to improve their activity and reduce any associated side effects. If this is successful, we enter the next stage, preclinical testing, which helps to understand how a drug candidate is transported and metabolized in an animal’s body and answers questions of safety and the dosage required for approval for clinical trials.

In the past years, we have used structural biology methods, including crystallography and cryoelectron microscopy (cryo-EM), and biophysical methods to investigate drug-target interaction details at atomic levels, which take more time and cost more. When the deep neural networks AlphaFold 2 (AF2) and RoseTTAFold (RF) came out in 2021, they raised a revolution in the modeling of protein structures and their interactions, and in a faster and cheaper way, they enable wide applications in the field of protein design and medicine. However, AF2 and RF have their limitations in the modeling of complexes, with low accuracy in structure prediction. Recently, in early 2024, the introduction of the AF3 model with substantially updated diffusion-based architecture1 and RF All-Atom (RFAA)2 has greatly changed this situation, and the model enables the joint structure prediction of complexes including proteins, nucleic acids, small molecules, ions, and modified residues. The AF3 and RFAA models have taken a large step toward understanding the complex atomic interactions of biological systems.

Computational techniques, including AI, are crucial in both academic and industrial fields,3 and they can substantially speed up the drug discovery process (Figure 1). Before AF3 and RFAA came out, the scientists who work on drug discovery mainly used classical molecular docking tools like Vina for virtual drug screening, and now, we can do more accurate and powerful drug screening with AF3 and RFAA, which outperform the classical tools according to the data shown in their papers.1,2 This will shorten the time for the discovery and preclinical stages, which usually take an average of 6 years (Figure 1). Of note, the accuracy of docking small molecules into the protein still needs to be further improved.

We also noticed that AF3 has limitations with respect to stereochemistry, hallucinations, dynamics, and accuracy for certain targets. There are two main classes of violations in stereochemistry, chirality and clash, and we should be cautious about these problems. In addition, the diffusion-based AF3 could introduce spurious structural order in the disordered region of a protein structure. A key limitation of the AF3 model is that it cannot predict the dynamic conformations of biological molecule systems in solution, and in some cases, the modeled conformational state cannot represent the correct or comprehensive facts given the specified ligands and other inputs. For example, as shown in the paper by Abramson et al.,1 the E3 ubiquitin ligases adopt an open conformation in apo state and have been observed with a closed conformation upon ligand binding. However, AF3 exclusively predicts the closed conformation for both apo and bound states.

We have been cautious in stating that AF3 cannot fully replace experimental methods in drug development, but it can provide more possibilities and selections that can be assayed by experimental methods. We believe that the predictions from AF3 can help to break through our concept barrier to develop some groundbreaking drugs, for example, in my experience, the identification of novel drug targets and development of broad-spectrum antiviral drugs for the preparedness of potential disease X pandemics.4,5 It is a pity that AF3 has not been open sourced yet, which will greatly hamper its wide use in the academic field and indispensable real-time feedback that will further help improve it. In the future, we should combine both computational and experimental methods together, and the data from experimental methods would further train computational tools like AF3 and make them more powerful.

Acknowledgments

Y.S. is supported by the 10.13039/501100012166 National Key Research and Development Program of China (2021YFC2300200 and 2021YFC2300700 ) and the 10.13039/501100001809 National Natural Science Foundation of China (NSFC) (32192452 and 82241076 ).

Declaration of interests

The author declares no competing interests.
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References

1 Abramson J. Adler J. Dunger J. Accurate structure prediction of biomolecular interactions with AlphaFold 3 Nature 630 2024 493 500 10.1038/s41586-024-07487-w 38718835
2 Krishna R. Wang J. Ahern W. Generalized biomolecular modeling and design with RoseTTAFold All-Atom Science 384 6693 2024 eadl2528
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4 Shi Y. Breakthrough the concept barrier to develop broad-spectrum antiviral countermeasures Nat. Rev. Microbiol. 22 2024 457 10.1038/s41579-024-01059-5 38769460
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