Meta-Analysis of Methods Used in Free Energy Calculations of Molecular Modeling in Drug Discovery
Keywords:
Meta-analysis, Free energy calculations, Drug discovery, Molecular modelingAbstract
Purpose: This study assesses the accuracy and reliability of free energy calculation (FEC) methods as quantitative tools in drug discovery by synthesizing empirical evidence on their predictive performance relative to experimental binding affinity data.
Methodology: A systematic review and meta-analysis were conducted following PRISMA guidelines. Five databases relevant to pharmaceutical and computational research were searched. Of 1,208 identified studies, 25 met the inclusion criteria and were analyzed (n = 25). The primary performance metric was the correlation coefficient between computed and experimentally measured binding affinities. A random-effects model was applied to estimate the pooled effect size while accounting for between-study variability.
Results: The meta-analysis produced a strong pooled correlation coefficient (r = 0.78; 95% CI: 0.74–0.81), indicating high predictive accuracy of FEC methods. The standard error (SE = 0.043) reflects robust estimation precision, and the large z-score (z = 24.29, p < 0.001) confirms statistical significance. The narrow confidence interval further demonstrates the consistency and reliability of FEC performance across studies.
Novelty and Contribution: This study provides one of the first quantitative meta-analytic evaluations of FEC methods in drug development. By integrating evidence across diverse computational frameworks and molecular systems, it offers strong empirical validation of FECs as reliable predictors of drug–target binding interactions.
Practical and Social Implications: The findings support broader adoption of FEC methods in pharmaceutical research, particularly for lead optimization and affinity prediction. Increased confidence in FEC accuracy can reduce experimental costs, accelerate drug development, and contribute to more efficient production of effective therapeutics.
References
Adelusi, T. I., Oyedele, A. Q. K., Boyenle, I. D., Ogunlana, A. T., Adeyemi, R. O., Ukachi, C. D., ... & Abdul-Hammed, M. (2022). Molecular modeling in drug discovery. Informatics in Medicine Unlocked, 29, 100880.
Asuero, A. G., Sayago, A., & González, A. G. (2006). The correlation coefficient: An overview. Critical reviews in analytical chemistry, 36(1), 41-59.
Caballero‐Lopez, R. A., & Moraal, H. (2004). Limitations of the force field equation to describe cosmic ray modulation. Journal of Geophysical Research: Space Physics, 109(A1).
Cai, C., Wang, S., Xu, Y., Zhang, W., Tang, K., Ouyang, Q., Lai, L. and Pei, J. (2020). Transfer learning for drug discovery. Journal of Medicinal Chemistry, 63(16), 8683-8694.
Carley, K. M. (1996). Validating computational models. Paper available at http://www. casos. cs. cmu. edu/publications/papers. php.
Chipot, C., & Pohorille, A. (2007). Free energy calculations (Vol. 86, pp. 159-184). Berlin: Springer.
Coudert, F. X. (2017). Reproducible research in computational chemistry of materials. Chemistry of Materials, 29(7), 2615-2617.
DerSimonian, R., & Laird, N. (1986). Meta-analysis in clinical trials. Controlled clinical trials, 7(3), 177-188.
Durrant, J. D., & McCammon, J. A. (2011). Molecular dynamics simulations and drug discovery. BMC biology, 9(1), 71.
Egger, M., Smith, G. D., Schneider, M., & Minder, C. (1997). Bias in meta-analysis detected by a simple, graphical test. bmj, 315(7109), 629-634.
Garbett, N. C., & Chaires, J. B. (2012). Thermodynamic studies for drug design and screening. Expert opinion on drug discovery, 7(4), 299-314.
Gilboa, S., Shirom, A., Fried, Y., & Cooper, C. (2008). A meta‐analysis of work demand stressors and job performance: examining main and moderating effects. Personnel psychology, 61(2), 227-271.
Grégoire, G., Derderian, F., & Le Lorier, J. (1995). Selecting the language of the publications included in a meta-analysis: is there a Tower of Babel bias?. Journal of clinical epidemiology, 48(1), 159-163.
Hajduk, P. J., & Greer, J. (2007). A decade of fragment-based drug design: strategic advances and lessons learned. Nature reviews Drug discovery, 6(3), 211-219.
Hardwicke, T. E., & Wagenmakers, E. J. (2023). Reducing bias, increasing transparency and calibrating confidence with preregistration. Nature Human Behaviour, 7(1), 15-26.
Higgins, J. P., & Thompson, S. G. (2002). Quantifying heterogeneity in a meta‐analysis. Statistics in medicine, 21(11), 1539-1558.
Hutchinson, L., & Kirk, R. (2011). High drug attrition rates—where are we going wrong? Nature reviews Clinical oncology, 8(4), 189-190.
Kim, R., & Skolnick, J. (2008). Assessment of programs for ligand binding affinity prediction. Journal of computational chemistry, 29(8), 1316-1331.
Klimovich, P. V., Shirts, M. R., & Mobley, D. L. (2015). Guidelines for the analysis of free energy calculations. Journal of Computer-Aided Molecular Design, 29(5), 397–411. https://doi.org/10.1007/s10822-015-9840-9
Kryshtafovych, A., Schwede, T., Topf, M., Fidelis, K., & Moult, J. (2019). Critical assessment of methods of protein structure prediction (CASP)—Round XIII. Proteins: Structure, Function, and Bioinformatics, 87(12), 1011-1020.
Kubinyi, H. (1997). QSAR and 3D QSAR in drug design Part 1: methodology. Drug discovery today, 2(11), 457-467.
Kufareva, I., Katritch, V., Stevens, R. C., & Abagyan, R. (2014). Advances in GPCR modeling evaluated by the GPCR Dock 2013 assessment: meeting new challenges. Structure, 22(8), 1120-1139.
Leelananda, S. P., & Lindert, S. (2016). Computational methods in drug discovery. Beilstein journal of organic chemistry, 12(1), 2694-2718.
Lu, N., & Kofke, D. A. (2001). Accuracy of free-energy perturbation calculations in molecular simulation. I. Modeling. The Journal of Chemical Physics, 114(17), 7303-7311.
Obaido, G., Agbo, F. J., Alvarado, C., & Oyelere, S. S. (2023). Analysis of attrition studies within the computer sciences. Ieee Access, 11, 53736-53748.
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., ... & Moher, D. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. bmj, 372.
Ponzoni, I., Sebastián-Pérez, V., Requena-Triguero, C., Roca, C., Martínez, M. J., Cravero, F., Díaz, M.F., Páez, J.A., Arrayás, R.G., Adrio, J. & Campillo, N. E. (2017). Hybridizing feature selection and feature learning approaches in QSAR modeling for drug discovery. Scientific reports, 7(1), 2403.
Price, A. J., Howard, S., & Cons, B. D. (2017). Fragment-based drug discovery and its application to challenging drug targets. Essays in biochemistry, 61(5), 475-484.
Prieto-Martínez, F. D., López-López, E., Juárez-Mercado, K. E., & Medina-Franco, J. L. (2019). Computational drug design methods—current and future perspectives. In silico drug design, 19-44.
Rising, K., Bacchetti, P., & Bero, L. (2008). Reporting bias in drug trials submitted to the Food and Drug Administration: review of publication and presentation. PLoS medicine, 5(11), e217.
Sadybekov, A. V., & Katritch, V. (2023). Computational approaches streamlining drug discovery. Nature, 616(7958), 673-685.
Schneider, G. (2018). Automating drug discovery. Nature reviews drug discovery, 17(2), 97-113.
Scior, T., Bender, A., Tresadern, G., Medina-Franco, J. L., Martínez-Mayorga, K., Langer, T., Cuanalo-Contreras, & Agrafiotis, D. K. (2012). Recognizing pitfalls in virtual screening: a critical review. Journal of chemical information and modeling, 52(4), 867-881.
Shirts, M. R., & Chodera, J. D. (2008). Statistically optimal analysis of samples from multiple equilibrium states. The Journal of chemical physics, 129(12).
Shukla, R., & Tripathi, T. (2020). Molecular dynamics simulation of protein and protein–ligand complexes. In Computer-aided drug design (pp. 133-161). Singapore: Springer Singapore.
Valentin, F., & Jensen, R. L. (2007). Effects on academia-industry collaboration of extending university property rights. The Journal of Technology Transfer, 32(3), 251-276.
Wang, L., Wu, Y., Deng, Y., Kim, B., Pierce, L., Krilov, G., Lupyan, D., Robinson, S., Dahlgren, M. K., Greenwood, J., Romero, D. L., Masse, C., Knight, J. L., Steinbrecher, T., Beuming, T., Damm, W., Harder, E., Sherman, W., Brewer, M., Wester, R., Murcko, M., Frye, L., Farid, R., Lin, T., Mobley, D. L., Jorgensen, W. L., Berne, B. J., Friesner, R. A., & Abel, R. (2015). Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field. Journal of the American Chemical Society, 137(7), 2695–2703. https://doi.org/10.1021/ja512751q
Zhang, P., Shen, L., & Yang, W. (2018). Solvation free energy calculations with quantum mechanics/molecular mechanics and machine learning models. The Journal of Physical Chemistry B, 123(4), 901-908.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Elicit Journal of Science Research
This work is licensed under a Creative Commons Attribution 4.0 International License.
