Computer Drug Development: from Traditional Modeling Methods to Language Models and Quantum Computing
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Abstract
Drug development is a central topic at the intersection of structural biology, biochemistry, and medicine, associated with significant challenges such as high cost (billions of dollars), low success rates (less than 10%), and extremely long development cycles (10-15 years). The Computer Aided Drug Design (CADD) system demonstrates tremendous advantages in solving these tasks and speeding up the process, making it an indispensable tool in the pharmaceutical industry and scientific research. The recent development of AlphaFold2 and AlphaFold3, the 2024 Nobel Prize winners, marks significant progress in the field of CADD. In addition to AlphaFold, various machine learning (ML) methods are revolutionizing various stages of drug development, from virtual screening to predictive modeling of drug interactions and treatment targets. Language models such as GPT models offer promising applications for developing research hypotheses and helping to interpret complex biological data. Quantum computing has the potential to solve complex molecular modeling and optimization problems that are currently unsolvable for classical computers, although their practical implementation is still in its early stages. This analytical review presents the latest developments in this field and evaluates the possibilities presented by machine learning, language models and quantum computing in CADD.
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References
Doytchinova, I. (2022). Drug design—past, present, future. Molecules 27, 1496.
Gorgulla, C., Boeszoermenyi, A., Wang, Z.-F., Fischer, P.D., Coote, P.W., Padmanabha Das, K.M., Malets, Y.S., Radchenko, D.S., Moroz, Y.S.,Scott, D.A., et al. (2020). An open-source drug discovery platform enables ultra-large virtual screens. Nature 580, 663–668.
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., _Zı´dek, A., Potapenko, A., et al. (2021). Highly accurate protein structure prediction with alphafold. Nature 596,583
Livermore Lab and BridgeBio Announce Human Trials for HPC-Discovered Cancer Drug, https://www.llnl.gov/article/51336/llnl-bridgebio-announcetrials-supercomputing-discovered-cancer-drug 2024 Accessed: 2024-06-07.
Yang, C., Chen, E.A., and Zhang, Y. (2022). Protein–ligand docking in the machine-learning era. Molecules 27, 4568
B. Zhong, X. Su, M. Wen, S. Zuo, L. Hong, J. Lin, Parafold: Paralleling alphafold for large-scale pre-dictions, in: International Conference on High Performance Computing in Asia-Pacific Region Workshops, pp. 1–9.
Evans, R., O’Neill, M., Pritzel, A., Antropova, N., Senior, A.W., Green, T., Zı´dek, A., Bates, R., Blackwell, S., Yim, J., et al. (2021). Protein complex prediction with alphafold-multimer. Preprint at bioRxiv. https://doi.org/10.1101/2021.10.04.463034
Abramson, J., Adler, J., Dunger, J., Evans, R., Green, T., Pritzel, A., Ronneberger, O., Willmore, L., Ballard, A.J., Bambrick, J., et al. (2024). Accurate structure prediction of biomolecular interactions with alphafold 3. Nature 630, 493–500.
Keshavarzi Arshadi, A., Webb, J., Salem, M., Cruz, E., Calad-Thomson, S., Ghadirian, N., Collins, J., Diez-Cecilia, E., Kelly, B., Goodarzi, H., and Yuan, J.S. (2020). Artificial intelligence for covid-19 drug discovery and vaccine development. Front. Artif. Intell. 3, 65.
Muratov EN, Bajorath J, Sheridan RP et al (2020) QSAR without borders. Chem Soc Rev 49:3525–3564. https:// doi. org/ 10. 1039/ D0CS0 0098A
Siramshetty VB, Nguyen D-T, Martinez NJ et al (2020) Critical assessment of artificial intelligence methods for prediction of hERG channel inhibition in the “Big Data” Era. J Chem Inf Model 60:6007–6019. https:// doi. org/10. 1021/ acs. jcim. 0c008 84
Jain S, Siramshetty VB, Alves VM et al (2021) Large-scale modeling of multispecies acute toxicity end points using consensus of multitask deep learning methods. J Chem Inf Model 61:653–663. https:// doi. org/ 10.1021/ acs. jcim. 0c011 64
Casanova-Alvarez O, Morales-Helguera A, Cabrera-Pйrez MБ et al (2021) A novel automated framework for QSAR modeling of highly imbalanced leishmania high-throughput screening data. J Chem Inf Model 61:3213–3231. https:// doi. org/ 10. 1021/ acs. jcim. 0c014 39
Neves BJ, Braga RC, Melo-Filho CC et al (2018) QSAR-based virtual screening: advances and ap-plications in drug discovery. Front Pharmacol 9
Spiegel J, Senderowitz H (2020) Evaluation of QSAR equations for virtual screening. Int J Mol Sci 21:7828. https:// doi. org/ 10. 3390/ ijms2 12178 28
Matveieva M, Polishchuk P (2021) Benchmarks for interpretation of QSAR models. J Cheminformatics 13:41. https:// doi. org/ 10. 1186/s13321- 021- 00519-x
Wellnitz J, Martin H-J, Anwar Hossain M et al (2024) STOPLIGHT: a hit scoring calculator. J Chem Inf Model 64:4387–4391. https:// doi. org/ 10. 1021/acs. jcim. 4c004 12
Pourbasheer, E., Aalizadeh, R. & Ganjali, M. R. Qsar study of ck2 inhibitors by ga-mlr and ga-svm methods. Arab. J.Chem. 12, 2141–2149 (2019).
Kim, B. C., Joe, D., Woo, Y., Kim, Y. & Yoon, G. Extension of pqsar: Ensemble model generated by random forest and partial least squares regressions. IEEE Access 8, 180087–180099 (2020).
Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., and Madani, A. (2023). Progen2: exploring the boundaries of protein language models. Cell Syst.14, 968–978.e3.
Ferruz, N., Schmidt, S., and Ho¨ cker, B. (2022). Protgpt2 is a deep unsupervised language model for protein design. Nat. Commun. 13, 4348.
Unsal, S., Atas, H., Albayrak, M., Turhan, K., Acar, A.C., and Dogan, T. (2022). Learning functional properties of proteins with language models.Nat. Mach. Intell. 4, 227–245.
Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al. (2023). Llama 2: Open foundation and fine-tuned chat models. Preprint at arXiv. https://doi.org/10.48550/arXiv.2307.09288
Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A.,Brown, A.R., Santoro, A., Gup-ta, A., Garriga-Alonso, A., et al. (2022).Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. Preprint at arXiv. https://doi.org/10.48550/arXiv2206.04615
Lu, J., and Zhang, Y. (2022). Unified deep learning model for multitask reaction predictions with explanation. J. Chem. Inf. Model. 62, 1376–1387
Bloch, F. (1946). Nuclear induction. Phys. Rev. 70, 460–474.
Benedetti, M., Lloyd, E., Sack, S. & Fiorentini, M. Parameterized quantum circuits as machine learning models. Quantum Sci. Technol. 4, 043001 (2019).
Biamonte, J. et al. Quantum machine learning. Nature 549, 195–202 (2017).
Tsang, S. L., West, M. T., Erfani, S. M. & Usman, M. Hybrid quantum-classical generative adversarial network for high resolution image generation. arXiv preprint arXiv:2212.11614 (2022).
Huang, H.-L. et al. Experimental quantum generative adversarial networks for image generation. Phys. Rev. Appl. 16, 024051 (2021).
Suzuki, T. & Katouda, M. Predicting toxicity by quantum machine learning. J. Phys. Commun. 4, 125012 (2020).
Wu, S. L. et al. Application of quantum machine learning using the quantum kernel algorithm on high energy physics analysis at the lhc. Phys. Rev. Res. 3, 033221 (2021).
Havlíˇcek, V. et al. Supervised learning with quantum-enhanced feature spaces. Nature 567, 209–212 (2019).
Batra, K. et al. Quantum machine learning algorithms for drug discovery applications. J. chemical information modeling 61, 2641–2647 (2021).
Liu, Y., Arunachalam, S. & Temme, K. A rigorous and robust quantum speed-up in supervised ma-chine learning. Nat. Phys. 17, 1013–1017 (2021).
Du, Y., Hsieh, M.-H., Liu, T. & Tao, D. Expressive power of parametrized quantum circuits. Phys. Rev. Res. 2, 033125 (2020).
Cao, Y., Romero, J. & Aspuru-Guzik, A. Potential of quantum computing for drug discovery. IBM J. Res. Dev. 62, 6–1 (2018).
Kao, P.-Y. et al. Exploring the advantages of quantum generative adversarial networks in generative chemistry. J. Chem. Inf. Model. (2023).
Domingo, L., Djukic, M., Johnson, C. & Borondo, F. Hybrid quantum-classical convolutional neural networks to improve molecular protein binding affinity predictions. arXiv preprint arXiv:2301.06331 (2023).
Hekler, E.B. et al. Why we need a small data paradigm. BMC medicine 17, 1–9 (2019).
Jolliffe, I.T. Principal component analysis for special types of data (Springer, 2002).
Wei-Yin Chiang, Po-Yu Kao, Tzu-Lan Yeh, Ya-Chu Yang, Yen-Chu Lin,Alex Zhavoronkov Enhanc-ing Drug Discovery: Quantum Machine Learning for QSAR Prediction with Incomplete Data arXiv:2501.13395v1 [quant-ph] 23 Jan 2025
Rogers, D. & Hahn, M. Extended-connectivity fingerprints. J. chemical information modeling 50, 742–754 (2010).
Zeng, X. et al. Accurate prediction of molecular properties and drug targets using a self-supervised image representation learning framework. Nat. Mach. Intell. 4, 1004–1016 (2022).
Landrum, G. et al. rdkit/rdkit: 2022_09_1b1 (q3 2022) release, DOI: 10.5281/zenodo.7179566 (2022).
Arute, F., Arya, K., Babbush, R., Bacon, D., Bardin, J.C., Barends, R., Biswas,R., Boixo, S., Brandao, F.G.S.L., Buell, D.A., et al. (2019). Quantum supremacy using a programmable superconducting processor. Nature 574, 505–510.
Shor, P.W. (1995). Scheme for reducing decoherence in quantum computer memory. Phys. Rev. 52, R2493–R2496.
Grimsley, H.R., Economou, S.E., Barnes, E., and Mayhall, N.J. (2019). Anadaptive variational algo-rithm for exact molecular simulations on a quantumcomputer. Nat. Commun. 10, 3007.
Cong, I., Choi, S., and Lukin, M.D. (2019). Quantum convolutional neural networks. Nat. Phys. 15, 1273–1278.
Cao, Y., Romero, J., and Aspuru-Guzik, A. (2018). Potential of quantum computing for drug discov-ery. IBM J. Res. Dev. 62, 1–6.
Outeiral, C., Strahm, M., Shi, J., Morris, G.M., Benjamin, S.C., and Deane, C.M. (2021). The pro-spects of quantum computing in computational molecular biology. WIREs Comput. Mol. Sci. 11, e1481.
Kandala, A., Mezzacapo, A., Temme, K., Takita, M., Brink, M., Chow, J.M., and Gambetta, J.M. (2017). Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets. Nature 549, 242–246.
Li, Y., Cui, X., Xiong, Z., Liu, B., Wang, B.-Y., Shu, R., Qiao, N., and Yung, M.-H. (2024). Quantum molecular docking with quantum-inspired algorithm.Preprint at arXiv. https://doi.org/10.48550/arXiv.2404.08265
Shu, R., Liu, B., Xiong, Z., Cui, X., Li, Y., Cui, W., Yung, M.-H., and Qiao, N. (2024). Quantum-inspired machine learning for molecular docking. Preprint at arXiv. https://doi.org/10.48550/arXiv.2308.04098
Ding, Q.-M., Huang, Y.-M., and Yuan, X. (2024). Molecular docking via quantum approximate op-timization algorithm. Phys. Rev. Appl. 21,034036.
Santagati, R., Aspuru-Guzik, A., Babbush, R., Degroote, M., Gonza´ lez, L., Kyoseva, E., Moll, N., Oppel, M., Parrish, R.M., Rubin, N.C., et al. (2024).Drug design on quantum computers. Nat. Phys. 20, 549–557.
Preskill, J. (2018). Quantum computing in the nisq era and beyond. Quantum 2, 79.
Bharti, K., Cervera-Lierta, A., Kyaw, T.H., Haug, T., Alperin-Lea, S., Anand,A., Degroote, M., Heim-onen, H., Kottmann, J.S., Menke, T., et al. (2022).Noisy intermediate-scale quantum algorithms. Rev. Mod. Phys. 94,015004.
“Boehringer Ingelheim - Life Forward,” Jan. 2024, https://www.boehringer-ingelheim.com
G. Kumar, S.Yadav, A.Aukherjee,V.Hassija, M. Guizani. Recent Advances in Quantum Computing for Drug Discovery and Development DOI: 10.1109/ACCESS.2024.3376408
“Quantum Computing,” Jan. 2024, https://www.rigetti.com
“Merck | Home,” Jan. 2024, [Online; accessed 31. Jan. 2024]. [Online] Available: https://www.merck.com
Home, Jan. 2024, https://zapata.ai
Vertex Pharmaceuticals | Home https://www.vrtx.com
Building the Best Solid-State Battery| Quantum Scape, Oct.2023, https://www.quantumscape.com