# Topics and Trends in Most Cited Computational Drug Discovery Methods Papers, Class of 2026

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## What topics and trends defined most-cited Computational Drug Discovery Methods research in the Class of 2026?

Computational drug discovery is rapidly transitioning from static screening to multi-objective generative AI. De novo molecular design surged fivefold to lead the cohort, supported by graph neural networks and 3D diffusion models. Concurrently, physics-informed molecular dynamics and automated ADMET profiling expanded significantly, while traditional standalone drug-target prediction declined.

## At a glance

| Fact | Value |
| --- | --- |
| Field | Computational Drug Discovery Methods |
| Cohort label | Class of 2026 (2024 publications) |
| Papers analyzed | 13,632 |
| Papers ranked | 20 |
| Top topics in ranked papers | De novo molecular design, virtual screening, binding affinity prediction, graph neural networks |
| Publication window | Jan 1, 2024 – Dec 31, 2024 |
| Eligibility | Research articles; reviews excluded |
| Citation window | 18 months post-publication |
| 18m citation range | 86–583 |
| Data source | OpenAlex · Retrieved July 2026 |
| License | CC BY 4.0 |

## Rankings

| Rank | Title | Authors | Corresponding authors | Affiliation | Journal | 18m citations | DOI |
| ---: | --- | --- | --- | --- | --- | ---: | --- |
| 1 | ProTox 3.0: a webserver for the prediction of toxicity of chemicals | Priyanka Banerjee, Emanuel Kemmler, Mathias Dunkel, Robert Preissner | Priyanka Banerjee | Charité - University Medicine Berlin, Germany | Nucleic Acids Research | 583 | 10.1093/nar/gkae303 |
| 2 | Practical guide to <scp>SHAP</scp> analysis: Explaining supervised machine learning model predictions in drug development | Ana Victoria Ponce-Bobadilla, Vanessa Schmitt, Corinna S Maier, Sven Mensing, Sven Stodtmann | Sven Stodtmann | AbbVie Deutschland GmbH & Co. KG, Germany | Clinical and Translational Science | 508 | 10.1111/cts.70056 |
| 3 | ADMETlab 3.0: an updated comprehensive online ADMET prediction platform enhanced with broader coverage, improved performance, API functionality and decision support | Li Fu, Shaohua Shi, Jiacai Yi, Ningning Wang, Yuanhang He, Zhenxing Wu, Jinfu Peng, Youchao Deng, Wenxuan Wang, Chengkun Wu, Aiping Lyu, Xiangxiang Zeng, Wentao Zhao, Tingjun Hou, Dongsheng Cao | Tingjun Hou | Central South University, China | Nucleic Acids Research | 446 | 10.1093/nar/gkae236 |
| 4 | SwissDock 2024: major enhancements for small-molecule docking with Attracting Cavities and AutoDock Vina | Marine Bugnon, Ute F Röhrig, Mathilde Goullieux, Marta A S Perez, Antoine Daina, Olivier Michielin, Vincent Zoete | Olivier Michielin | SIB Swiss Institute of Bioinformatics, Switzerland | Nucleic Acids Research | 265 | 10.1093/nar/gkae300 |
| 5 | Leveraging large language models for predictive chemistry | Kevin Maik Jablonka, Philippe Schwaller, Andres Ortega‐Guerrero, Berend Smit | Berend Smit | Friedrich Schiller University Jena, Germany | Nature Machine Intelligence | 180 | 10.1038/s42256-023-00788-1 |
| 6 | Structure-based drug design with equivariant diffusion models | Arne Schneuing, Charles Harris, Yuanqi Du, Kieran Didi, Arian Jamasb, Ilia Igashov, Weitao Du, Carla Gomes, Tom L Blundell, Pietro Lio, Max Welling, Michael Bronstein, Bruno Correia | Arne Schneuing, Bruno E. Correia | École Polytechnique Fédérale de Lausanne, Switzerland | Nature Computational Science | 164 | 10.1038/s43588-024-00737-x |
| 7 | Open Targets Platform: facilitating therapeutic hypotheses building in drug discovery | Annalisa Buniello, Daniel Suveges, Carlos Cruz-Castillo, Manuel Bernal Llinares, Helena Cornu, Irene Lopez, Kirill Tsukanov, Juan María Roldán-Romero, Chintan Mehta, Luca Fumis, Graham McNeill, James D Hayhurst, Ricardo Esteban Martinez Osorio, Ehsan Barkhordari, Javier Ferrer, Miguel Carmona, Prashant Uniyal, Maria J Falaguera, Polina Rusina, Ines Smit, Jeremy Schwartzentruber, Tobi Alegbe, Vivien W Ho, Daniel Considine, Xiangyu Ge, Szymon Szyszkowski, Yakov Tsepilov, Maya Ghoussaini, Ian Dunham, David G Hulcoop, Ellen M McDonagh, David Ochoa | Annalisa Buniello | Open Targets, United Kingdom | Nucleic Acids Research | 158 | 10.1093/nar/gkae1128 |
| 8 | An artificial intelligence accelerated virtual screening platform for drug discovery | Guangfeng Zhou, Domnita-Valeria Rusnac, Hahnbeom Park, Daniele Canzani, Hai Minh Nguyen, Lance Stewart, Matthew F Bush, Phuong Tran Nguyen, Heike Wulff, Vladimir Yarov-Yarovoy, Ning Zheng, Frank DiMaio | Ning Zheng, Frank DiMaio | University of Washington, United States | Nature Communications | 155 | 10.1038/s41467-024-52061-7 |
| 9 | VFDB 2025: an integrated resource for exploring anti-virulence compounds | Siyu Zhou, Bo Liu, Dandan Zheng, Lihong Chen, Jian Yang | Siyu Zhou | Chinese Academy of Medical Sciences & Peking Union Medical College, China | Nucleic Acids Research | 151 | 10.1093/nar/gkae968 |
| 10 | ADMET-AI: a machine learning ADMET platform for evaluation of large-scale chemical libraries | Kyle Swanson, Parker Walther, Jeremy Leitz, Souhrid Mukherjee, Joseph C Wu, Rabindra V Shivnaraine, James Zou | Kyle Swanson, Rabindra V. Shivnaraine, James Zou | Stanford University, United States | Bioinformatics | 146 | 10.1093/bioinformatics/btae416 |
| 11 | Artificial intelligence alphafold model for molecular biology and drug discovery: a machine-learning-driven informatics investigation | Song-Bin Guo, Yuan Meng, Liteng Lin, Zhen-Zhong Zhou, Hai-Long Li, Xiao-Peng Tian, Wei-Juan Huang | Song‐Bin Guo, Weijuan Huang | Sun Yat-Sen University Cancer Center, China | Molecular Cancer | 146 | 10.1186/s12943-024-02140-6 |
| 12 | A foundation model for clinician-centered drug repurposing | Kexin Huang, Payal Chandak, Qianwen Wang, Shreyas Havaldar, Akhil Vaid, Jure Leskovec, Girish N Nadkarni, Benjamin S Glicksberg, Nils Gehlenborg, Marinka Zitnik | Marinka Žitnik | Harvard Medical School, United States | Nature Medicine | 144 | 10.1038/s41591-024-03233-x |
| 13 | Deep-PK: deep learning for small molecule pharmacokinetic and toxicity prediction | Yoochan Myung, Alex G C de Sá, David B Ascher | Alex G. C. de Sá, David B. Ascher | The University of Queensland, Australia | Nucleic Acids Research | 125 | 10.1093/nar/gkae254 |
| 14 | AlphaFold predictions of fold-switched conformations are driven by structure memorization | Devlina Chakravarty, Joseph W Schafer, Ethan A Chen, Joseph F Thole, Leslie A Ronish, Myeongsang Lee, Lauren L Porter | Lauren L. Porter | National Institutes of Health, United States | Nature Communications | 112 | 10.1038/s41467-024-51801-z |
| 15 | Reinvent 4: Modern AI–driven generative molecule design | Hannes H Loeffler, Jiazhen He, Alessandro Tibo, Jon Paul Janet, Alexey Voronov, Lewis H Mervin, Ola Engkvist | Hannes H. Loeffler | AstraZeneca, Sweden | Journal of Cheminformatics | 99 | 10.1186/s13321-024-00812-5 |
| 16 | Machine learning-aided generative molecular design | Yuanqi Du, Arian R. Jamasb, Jeff Guo, Tianfan Fu, Charles B. Harris, Yingheng Wang, Chenru Duan, Píetro Lió, Philippe Schwaller, Tom L. Blundell | Philippe Schwaller, Tom L. Blundell | Cornell University, United States | Nature Machine Intelligence | 93 | 10.1038/s42256-024-00843-5 |
| 17 | Equivariant 3D-conditional diffusion model for molecular linker design | Ilia Igashov, H. Stärk, Clément Vignac, Arne Schneuing, Víctor García Satorras, Pascal Frossard, Max Welling, Michael M. Bronstein, Bruno E. Correia | Bruno E. Correia | École Polytechnique Fédérale de Lausanne, Switzerland | Nature Machine Intelligence | 91 | 10.1038/s42256-024-00815-9 |
| 18 | DynamicBind: predicting ligand-specific protein-ligand complex structure with a deep equivariant generative model | Wei Lu, Jixian Zhang, Weifeng Huang, Ziqiao Zhang, Xiangyu Jia, Zhenyu Wang, Leilei Shi, Chengtao Li, Peter G Wolynes, Shuangjia Zheng | Wei Lu, Jixian Zhang, Shuangjia Zheng | Galixir Technologies, China | Nature Communications | 88 | 10.1038/s41467-024-45461-2 |
| 19 | Generative AI for designing and validating easily synthesizable and structurally novel antibiotics | Kyle Swanson, Gary Liu, Denise B. Catacutan, Autumn Arnold, James Zou, J Stokes | James Zou | Stanford University, United States | Nature Machine Intelligence | 88 | 10.1038/s42256-024-00809-7 |
| 20 | AlphaFold2 structures guide prospective ligand discovery | Jiankun Lyu, Nicholas Kapolka, Ryan Gumpper, Assaf Alon, Liang Wang, Manish K Jain, Ximena Barros-Álvarez, Kensuke Sakamoto, Yoojoong Kim, Jeffrey DiBerto, Kuglae Kim, Isabella S Glenn, Tia A Tummino, Sijie Huang, John J Irwin, Olga O Tarkhanova, Yurii Moroz, Georgios Skiniotis, Andrew C Kruse, Brian K Shoichet, Bryan L Roth | Jiankun Lyu, Nicholas J. Kapolka, Ryan H. Gumpper, Assaf Alon, Liang Wang, Georgios Skiniotis, Andrew C. Kruse, Brian K. Shoichet, Bryan L. Roth | University of California, San Francisco, United States | Science | 86 | 10.1126/science.adn6354 |

## Topic trends

### What Topics Define the Class of 2026?

The Class of 2026 in computational drug discovery reflects a major pivot toward generative molecular design and AI-driven predictive screening. De novo molecular design emerges as the single most dominant theme, present in 20% of high-impact papers (10 out of 50). Classical structure-based methodologies—including molecular docking, virtual screening, and binding affinity prediction—remain fundamental pillars, each featured in 18% of ranked studies. However, these traditional approaches are increasingly augmented by deep learning frameworks such as graph neural networks (14%) and 3D equivariant diffusion models (8%).

A key hallmark of this cohort is the holistic integration of property optimization early in the generative pipeline. Rather than treating candidate selection as a post-hoc filtering step, top-ranked studies embed ADMET predictions—spanning absorption, distribution, metabolism, and toxicity—directly into molecular generation workflows. Additionally, machine learning interatomic potentials (8%) and high-throughput molecular dynamics simulations (10%) are expanding the accuracy of conformational sampling and free energy landscapes. Together, these trends demonstrate that computational drug discovery has evolved from static virtual screening toward dynamic, multi-objective generative frameworks capable of designing synthetically accessible, drug-like candidates.

*Leading research themes*

### How Did Topics Shift from the Class of 2025 to the Class of 2026?

Comparing the Class of 2025 to the Class of 2026 reveals a decisive structural shift from single-target hit identification toward end-to-end generative design. The most dramatic surge occurred in de novo molecular design, which expanded fivefold from 4% of papers in 2023 to 20% in 2024. Next-generation deep learning architectures experienced rapid adoption: graph neural networks grew 2.33-fold (6% to 14%), while 3D equivariant diffusion models and machine learning interatomic potentials emerged as new frontline methodologies, appearing in 8% of ranked publications.

Predictive biophysics and safety profiling also recorded significant gains. Molecular dynamics simulations grew 2.5-fold (4% to 10%), while comprehensive ADMET evaluations and conformational sampling quadrupled (2% to 8%). In contrast, legacy methodologies such as standalone drug-target interaction modeling dropped from 26% to 14%, and basic hit identification declined from 10% to 4%. This shift indicates that the field is moving away from isolated binding predictions toward unified AI architectures that simultaneously optimize target binding affinity, synthetic accessibility, and pharmacokinetic profiles in a single computational workflow.

*How topics shifted year over year*

## Cite this ranking

```
Pepkio Research Index (PRI). Topics and Trends in Most Cited Computational Drug Discovery Methods Papers, Class of 2026. https://pri.pepkio.com/top-papers/computational-drug-discovery-methods/2026. Accessed 2026-07-22.

Zheng Su, Tinsley Li, Thematic Shifts in Early-High-Impact Cancer Genomics and Diagnostics Research: A Bibliometric and Semantic Analysis. bioRxiv 2026.07.04.736459; doi: https://doi.org/10.64898/2026.07.04.736459
```