AI-Based Protein Structure Prediction in Drug Discovery: Impacts and Challenges
Abstract
Proteins are the molecular machinery of the human body, and their malfunctioning is often responsible for diseases, making them crucial targets for drug discovery. The three-dimensional structure of a protein determines its biological function, its conformational state determines substrates, cofactors, and protein binding. Rational drug discovery employs engineered small molecules to selectively interact with proteins to modulate their function. To selectively target a protein and to design small molecules, knowing the protein structure with all its specific conformation is critical. Unfortunately, for a large number of proteins relevant for drug discovery, the three-dimensional structure has not yet been experimentally solved. Therefore, accurately predicting their structure based on their amino acid sequence is one of the grant challenges in biology. Recently, AlphaFold2, a machine learning application based on a deep neural network, was able to predict unknown structures of proteins with an unprecedented accuracy. Despite the impressive progress made by AlphaFold2, nature still challenges the field of structure prediction. In this Perspective, we explore how AlphaFold2 and related methods help make drug design more efficient. Furthermore, we discuss the roles of predicting domain-domain orientations, all relevant conformational states, the influence of posttranslational modifications, and conformational changes due to protein binding partners. We highlight where further improvements are needed for advanced machine learning methods to be successfully and frequently used in the pharmaceutical industry.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In an end-to-end hepatocellular carcinoma example using Pharma.AI, disease-gene analyses identified CDK20 as a target.
“Using an integrated platform (Pharma.AI), disease-gene analyses identified CDK20 as a target (PandaOmics), an AlphaFold2 model supplied the target structure, and a generative module (Chemistry42) produced approximately 9000 candidate molecules [102,103].”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [103]
Seven of the approximately 9000 candidate molecules were synthesized and tested; one showed promising activity in liver cancer cells and advanced as a lead, all within roughly 30 days from design initiation.
“Seven of these molecules were synthesized and tested; one showed promising activity in liver cancer cells and advanced as a lead, all within roughly 30 days from design initiation.”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [103]
The generative module Chemistry42 produced approximately 9000 candidate molecules.
“Using an integrated platform (Pharma.AI), disease-gene analyses identified CDK20 as a target (PandaOmics), an AlphaFold2 model supplied the target structure, and a generative module (Chemistry42) produced approximately 9000 candidate molecules [102,103].”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [103]
Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works
The Phyre2 web portal for protein modeling, prediction and analysis
Kelley, L. A., Mezulis, S., Yates, C. M., Wass, M. N., & Sternberg, M. J. E. (2015) · Nature Protocols · doi:10.1038/nprot.2015.053 · Cited by 9,652 · see citing works
Development and validation of a genetic algorithm for flexible docking 1 1Edited by F. E. Cohen
Jones, G., Willett, P., Glen, R. C., Leach, A. R., & Taylor, R. (1997) · Journal of Molecular Biology · doi:10.1006/jmbi.1996.0897 · Cited by 6,763 · see citing works
Protein complex prediction with AlphaFold-Multimer
Evans, R., O’Neill, M., Pritzel, A., Antropova, N., Senior, A., Green, T., Žídek, A., Bates, R., Blackwell, S., Yim, J., et al. (2021) · doi:10.1101/2021.10.04.463034 · Cited by 3,808 · see citing works
DeepTox: toxicity prediction using deep learning
Mayr, A., Klambauer, G., Unterthiner, T., & Hochreiter, S. (2016) · Frontiers in Environmental Science · doi:10.3389/fenvs.2015.00080 · Cited by 1,081 · see citing works
Allosteric modulators of GPCRs: a novel approach for the treatment of CNS disorders
Jeffrey Conn, P., Christopoulos, A., & Lindsley, C. W. (2009) · Nature Reviews Drug Discovery · doi:10.1038/nrd2760 · Cited by 1,063 · see citing works
Improved prediction of protein-protein interactions using AlphaFold2
Bryant, P., Pozzati, G., & Elofsson, A. (2021) · doi:10.1101/2021.09.15.460468 · Cited by 1,025 · see citing works
Lu, H., Zhou, Q., He, J., Jiang, Z., Peng, C., Tong, R., & Shi, J. (2020) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-020-00315-3 · Cited by 891 · see citing works
Advances in protein structure prediction and design
Kuhlman, B., & Bradley, P. (2019) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-019-0163-x · Cited by 806 · see citing works
CCNet: Criss-Cross Attention for Semantic Segmentation
Huang, Z., Wang, X., Wei, Y., Huang, L., Shi, H., Liu, W., & Huang, T. S. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3007032 · Cited by 524 · see citing works
Single-sequence protein structure prediction using language models from deep learning
Chowdhury, R., Bouatta, N., Biswas, S., Rochereau, C., Church, G. M., Sorger, P. K., & AlQuraishi, M. (2021) · doi:10.1101/2021.08.02.454840 · Cited by 455 · see citing works
Single-sequence protein structure prediction using a language model and deep learning
Chowdhury, R., Bouatta, N., Biswas, S., Floristean, C., Kharkar, A., Roy, K., Rochereau, C., Ahdritz, G., Zhang, J., Church, G. M., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01432-w · Cited by 455 · see citing works
End-to-End Differentiable Learning of Protein Structure
AlQuraishi, M. (2019) · Cell Systems · doi:10.1016/j.cels.2019.03.006 · Cited by 371 · see citing works
Using AlphaFold to predict the impact of single mutations on protein stability and function
Pak, M. A., Markhieva, K. A., Novikova, M. S., Petrov, D. S., Vorobyev, I. S., Maksimova, E. S., Kondrashov, F. A., & Ivankov, D. N. (2023) · PLOS One · doi:10.1371/journal.pone.0282689 · Cited by 322 · see citing works
High‐accuracy protein structure prediction in <scp>CASP14</scp>
Pereira, J., Simpkin, A. J., Hartmann, M. D., Rigden, D. J., Keegan, R. M., & Lupas, A. N. (2021) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26171 · Cited by 314 · see citing works
SPEACH_AF: Sampling protein ensembles and conformational heterogeneity with Alphafold2
Stein, R. A., & Mchaourab, H. S. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1010483 · Cited by 258 · see citing works
Predicting free energy changes using structural ensembles
Benedix, A., Becker, C. M., de Groot, B. L., Caflisch, A., & Böckmann, R. A. (2009) · Nature Methods · doi:10.1038/nmeth0109-3 · Cited by 234 · see citing works
eToxPred: a machine learning-based approach to estimate the toxicity of drug candidates
Pu, L., Naderi, M., Liu, T., Wu, H.-C., Mukhopadhyay, S., & Brylinski, M. (2019) · BMC Pharmacology and Toxicology · doi:10.1186/s40360-018-0282-6 · Cited by 212 · see citing works
Specificity of Linear Motifs That Bind to a Common Mitogen-Activated Protein Kinase Docking Groove
Garai, Á., Zeke, A., Gógl, G., Törő, I., Fördős, F., Blankenburg, H., Bárkai, T., Varga, J., Alexa, A., Emig, D., et al. (2012) · Science Signaling · doi:10.1126/scisignal.2003004 · Cited by 177 · see citing works
rawMSA: End-to-end Deep Learning using raw Multiple Sequence Alignments
Mirabello, C., & Wallner, B. (2019) · PLOS ONE · doi:10.1371/journal.pone.0220182 · Cited by 105 · see citing works
The interferon regulatory factors and oncogenesis
Tanaka, N., & Taniguchi, T. (2000) · Seminars in Cancer Biology · doi:10.1006/scbi.2000.0310 · Cited by 85 · see citing works
Improved Docking of Protein Models by a Combination of Alphafold2 and ClusPro
Ghani, U., Desta, I., Jindal, A., Khan, O., Jones, G., Hashemi, N., Kotelnikov, S., Padhorny, D., Vajda, S., & Kozakov, D. (2021) · doi:10.1101/2021.09.07.459290 · Cited by 68 · see citing works
<scp>NMR</scp> hawk‐eyed view of <scp>AlphaFold2</scp> structures
Zweckstetter, M. (2021) · Protein Science · doi:10.1002/pro.4175 · Cited by 47 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery
Kuno, M., Osumi, H., Udagawa, S., Yoshikawa, K., Ooki, A., Shinozaki, E., Ishikawa, T., Oba, J., Yamaguchi, K., & Sakurada, K. (2025) · Current Oncology · doi:10.3390/curroncol32110588 · Cited by 7 · see citing works
- Authors
- Schauperl, M., & Denny, R. A.
- Affiliations
- Department of Computational Sciences HotSpot Therapeutics 50 Milk Street, Boston, Massachusetts 02110, United States
- Corresponding author
- Schauperl, Michael
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2022-06-21
- Volume
- 62
- Issue
- 13
- Pages
- 3142-3156
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W4283219703
- Cited by
- 122
- References
- 23
- Citations by year
- 2026: 21 · 2025: 33 · 2024: 30 · 2023: 31 · 2022: 7
- FWCI
- 2.63
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Journal of Chemical Information and Modeling (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- protein structure prediction, AlphaFold2, drug discovery, rational drug design, protein conformational states, post-translational modification, protein-ligand binding, deep learning, pharmaceutical industry
- MeSH terms
- Machine Learning, Humans, Protein Conformation, Proteins, Neural Networks, Computer, Computational Biology, Drug Discovery
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
