Integrating Machine Learning Models in Next-Gen Drug Discovery

Abstract

Artificial intelligence and machine learning are completely transforming how we discover new medicines. Rather than investing lots of time and money in the lab, we can provide predictions for a chemical\'s activity very quickly. This article will take a look at four papers looking at the application of AI in two key areas of drug discovery: predicting drug-likeness and predicting binding affinity/target engagement. The first two papers, Druggability of Pharmaceutical Compounds Using Lipinski Rules with Machine Learning (2024) and DrugMetric: Quantitative Drug-Likeness Scoring Based on Chemical Space Distance (2024), focus on identifying and scoring compounds based on discrete physical and chemical properties, employing two types of machine learning either supervised or unsupervised. The second two papers, Explainable Deep Drug-Target Representations for Binding Affinity Prediction (2022) and WideDTA: Prediction of Drug-Target Binding Affinity (2019), utilize deep learning techniques such as combined graph, sequence, and text based to determine binding affinity of bound proteins and ligands, a more sophisticated approach. Looking across the four papers, we note that machine learning models introducing randomness, such as Random Forest and XGBoost may be recommended as easy to follow approaches even when utilizing rules to predict whether or not something is drug-like. However, deep learning methods such as Graph Neural Networks (GNNs) and Convolutional Neural Networks (CNNs) are much better at inferring complex relationships between molecules when predicting their binding affinities. Nevertheless, the studies also support that there is a tradeoff between interpretability, transferability of the model to new data, and how long it takes to make a prediction. In summary, it appears that we will move towards a model that uses both interpretable machine learning and deep learning together to develop AI systems that facilitate discovery in drug development using data.

Introduction

The text discusses the application of artificial intelligence (AI) and machine learning (ML) in accelerating drug discovery, focusing on predicting drug-likeness and drug-target binding affinity. Traditional drug development is slow and costly, requiring extensive lab testing, but AI enables rapid computational evaluation of molecules’ properties, including absorption, distribution, metabolism, excretion (ADME), and binding affinity to protein targets.

Four key studies illustrate these advancements:

1. Druggability via Lipinski’s Rule of Five with ML – Machine learning models (Random Forest, XGBoost, Decision Trees) were used to predict adherence to RO5, achieving high accuracy (~99.9%), demonstrating the effectiveness of ensemble learning in screening drug candidates.

2. DrugMetric: Quantitative Drug-Likeness Scoring – An unsupervised framework combining Variational Autoencoders, Gaussian Mixture Models, and ensemble learning quantitatively scores drug-likeness, outperforming traditional methods like QED for virtual screening.

3. Explainable Deep Drug–Target Representations – A dual encoder model using Graph Neural Networks (GNNs) for ligands and CNNs for proteins predicts binding affinity, incorporating structural and sequential features while providing interpretable insights.

4. WideDTA – A text-based deep learning model predicting binding affinity from protein sequences and ligand SMILES using sequence “words,” outperforming previous character-based models like DeepDTA without requiring 3D structural information.

Collectively, these approaches show that AI and ML can efficiently evaluate drug-likeness and binding affinity, reduce experimental costs, and enhance the interpretability and precision of early-stage drug discovery.

Conclusion

A collective review of the four studies illustrates the transformative role of AI in modern drug discovery, ranging from drug-likeness prediction to binding affinity estimation. Machine learning models, notably the ensemble framework based on Lipinski, achieved very high predictive accuracy and interpretability, allowing for early-stage compound screening. In contrast, deep learning models, like DrugMetric\'s latent-space model, the GNN–CNN hybrid model, and WideDTA, provide evidence of data-driven learning of representation exposing complex protein–ligand interaction. These methods will be used together to showcase a substantial shift from traditional rule-based screening to automated data-centric prediction systems capable of modeling the chemical and biological relationship with unmatched precision. Additionally, the findings included a balanced compromise between performance and interpretability: classical ML models are interpretable, while deep architectures tend to better generalization and scalability. Inclusion of graph and sequence-based encoders further demonstrates rich molecular and biochemical representations as an important route to improving predictive depth by capturing true interaction dynamics. Taken together, these four articles communicate a common idea: if we are hoping to balance both scientific insight and computational efficiency for drug discovery in the future, drug discovery will eventually require synthesizing interpretable, rule-based principles with high-capacity deep learning paradigms.layer for the subsequent trajectory models. The Grad- CAM++ framework (2021) provides the missing piece of the puzzle in interpretability, as it presents visual justifications that clarify the reasoning behind the projections of neural networks and simultaneously increase the trust of the user. When these studies are compared, it becomes apparent that they have mutualistic capabilities: the object detectors are very accurate and provide a very good visual comprehension quickly but they cannot forecast very well; on the other hand, the trajectory models are very accurate in their movement predictions but they often lack transparency which is a disadvantage; and the explainability techniques such as Grad- CAM++ provide the understanding of the networks that’s the reason why they expose the area of the networks’ focus for the forecasts. Overall, the literature indicates that the future of self-driving vehicles will be based on the merging of highly accurate perception, contextually aware prediction, and transparent reasoning that is all done in one deep-learning pipeline. To conclude, the papers reviewed provide a clear way for the research intended to be carried out—the creation of an explainable trajectory-prediction framework that will not only be able to estimate the motion accurately but also effectively communicate the reasoning behind every prediction. This kind of combination is needed for making autonomous systems that technically dependable and valuable of human trust.

References

[1] Macal, C. M., & North, M. J. (2009). Agent-based modeling and simulation. Proceedings of the 2009 Winter Simulation Conference, 86–98. IEEE. https://doi.org/10.1109/WSC.2009.5429318 [2] An, G., Mi, Q., Dutta-Moscato, J., & Vodovotz, Y. (2009). Agent-based models in translational systems biology. Wiley Interdisciplinary Reviews: Systems Biology and Medicine, 1(2), 159–171. https://doi.org/10.1002/wsbm.45 [3] Vodovotz, Y., & An, G. (2019). Agent-based models of inflammation: A decade later. Wiley Interdisciplinary Reviews: Systems Biology and Medicine, 11(3), e1460. https://doi.org/10.1002/wsbm.1460 [4] Tong, X., Chen, J., Miao, H., Li, T., & Zhang, L. (2015). Development of an agent-based model (ABM) to simulate the immune system and integration of a regression method to estimate key ABM parameters by fitting experimental data. PLOS ONE, 10(11), e0141295. https://doi.org/10.1371/journal.pone.0141295 [5] Sivakumar, S., Mura, C., & Peirce, S. M. (2022). Integrating machine learning and agentbased modeling to study complex biological systems. Frontiers in Systems Biology, 2, 959665. https://doi.org/10.3389/fsysb.2022.959665 [6] Öztürk, H., Özgür, A., & Ozkirimli, E. (2019). DeepDTA: Deep drug–target binding affinity prediction. Bioinformatics, 34(17), i821–i829. tps://doi.org/10.1093/bioinformatics/bty593 [7] Öztürk, H., Özgür, A., & Ozkirimli, E. (2020). WideDTA: Prediction of drug–target binding affinity using word-based deep learning. Bioinformatics, 36(17), 4627–4635. https://doi.org/10.1093/bioinformatics/btaa626 [8] Gao, K. Y., Fokoue, A., Luo, H., Iyengar, A., Dey, S., & Zhang, P. (2018). Interpretable deep neural networks for predicting drug–target interactions. Frontiers in Pharmacology, 9, 1426. https://doi.org/10.3389/fphar.2018.01426 [9] Jiménez, J., Doerr, S., Martínez-Rosell, G., Rose, A. S., & De Fabritiis, G. (2018). DeepSite: Protein-binding site predictor using 3D-convolutional neural networks. Bioinformatics, 33(19), 3036–3042. https://doi.org/10.1093/bioinformatics/btx350 [10] Karimi, M., Wu, D., Wang, Z., & Shen, Y. (2019). DeepAffinity: Interpretable deep learning of compound–protein affinity through unified recurrent and convolutional neural networks. Bioinformatics, 35(18), 3329–3338. https://doi.org/10.1093/bioinformatics/btz111 [11] Liu, T., Lin, Y., Wen, X., Jorissen, R. N., & Gilson, M. K. (2007). BindingDB: A webaccessible database of experimentally determined protein–ligand binding affinities. Nucleic Acids Research, 35(Database issue), D198–D201. https://doi.org/10.1093/nar/gkl999 74 [12] Hopkins, A. L., & Groom, C. R. (2002). The druggable genome. Nature Reviews Drug Discovery, 1(9), 727–730. https://doi.org/10.1038/nrd892 [13] Lipinski, C. A. (2004). Lead- and drug-like compounds: The rule-of-five revolution. Drug Discovery Today: Technologies, 1(4), 337–341. [14] Walters, W. P., & Namchuk, M. (2003). Designing screens: How to make your hits a hit. Nature Reviews Drug Discovery, 2(4), 259–266. https://doi.org/10.1038/nrd1063 [15] Vamathevan, J., et al. (2019). Applications of machine learning in drug discovery and development. Nature Reviews Drug Discovery, 18(6), 463–477. https://doi.org/10.1038/s41573-019-0024-5 [16] Chen, L., Tan, X., Wang, D., Zhong, F., Liu, X., Yang, T., & Luo, X. (2020). TransformerCPI: Improving compound–protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments. Bioinformatics, 36(16), 4406–4414. https://doi.org/10.1093/bioinformatics/btaa524 [17] Rifaioglu, A. S., Atas, H., Martin, M. J., Cetin-Atalay, R., Atalay, V., & Dogan, T. (2020). Deep learning and machine intelligence in drug discovery: A comprehensive survey. Artificial Intelligence Review, 53, 295–347. https://doi.org/10.1007/s10462-018-09605-9 [18] Xie, T., & Grossman, J. C. (2018). Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Physical Review Letters, 120(14), 145301. https://doi.org/10.1103/PhysRevLett.120.145301 [19] Chou, C. H., & Voit, E. O. (2009). Recent developments in parameter estimation and structure identification of biochemical and genomic systems. Mathematical Biosciences, 219(2), 57–83. https://doi.org/10.1016/j.mbs.2009.03.004 [20] 20. Xiang, Y., & Gong, Y. (2021). Hybrid agent-based and machine learning approaches for biological system modeling: Challenges and opportunities. Computational and Structural Biotechnolog.

Copyright

Copyright © 2025 Dr. S. Gunasekaran , Mr. Nandu S Nair, Nazna N, Riya Marjum M R, Sanjay S, Thoiba M. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.