Papers
This section is about my interactions with research papers - most often they are a lot to read and parse through, but sometimes they are totally worth it. The first part, summaries, is where I have my own inter-pretations and takes on various papers that I have read. The second part, reading list, is a curated list of papers that I want to read (someday).
Summaries
I share summaries and opinions of papers that I’ve read, across multiple disciplines. Most of it will be concise key points that I find interesting, and perhaps some personal takes.
This is intended to serve as a platform to strike curiosity, initiate discussions and a catalog for my own reading. Feel free to connect with me and start a discussion on any statement that interests you, or even better, correct or teach me when I am wrong.
This format is inspired from Fan Pu’s website.
COMING SOON
Summaries are in the works...
Reading List
Atomistic Models
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1. Learning Long-Range Interactions in Equivariant Machine Learning Interatomic Potentials via Electronic Degrees of Freedom
Moin Uddin Maruf, Sungmin Kim, Zeeshan Ahmad
2025 -
2. Learning from the electronic structure of molecules across the periodic table
Manasa Kaniselvan, Benjamin Kurt Miller, Meng Gao, Juno Nam, Daniel S. Levine
2025-09-30 - 2025-09-30
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4. Reasoning-Enhanced Large Language Models for Molecular Property Prediction
Jiaxi Zhuang, Yaorui Shi, Jue Hou, Yunong He, Mingwei Ye, Mingjun Xu, Yuming Su et al.
2025-10-11 - 2025-10-10
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6. Beyond Scaling: Chemical Intuition as Emergent Ability of Universal Machine Learning Interatomic Potentials
Shinnosuke Hattori, Kohei Shimamura, Aiichiro Nakano, Rajiv K. Kalia, Priya Vashishta, Ken-ichi Nomura
2025-06-09 -
7. Data Generation for Machine Learning Interatomic Potentials and Beyond
Maksim Kulichenko, Benjamin Nebgen, Nicholas Lubbers, Justin S. Smith, Kipton Barros, Alice E. A. Allen, Adela Habib et al.
2024 -
8. Equivariant Spherical Transformer for Efficient Molecular Modeling
Junyi An, Xinyu Lu, Chao Qu, Yunfei Shi, Peijia Lin, Qianwei Tang, Licheng Xu et al.
2025-05-29 -
9. Studying Noncovalent Interactions in Molecular Systems with Machine Learning
Serhii Tretiakov, AkshatKumar Nigam, Robert Pollice
2025 -
10. E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
Batzner, Simon, Musaelian, Albert, Sun, Lixin, Geiger et al.
2022-05-04 -
11. Graph Neural Networks in Modern AI-Aided Drug Discovery
Odin Zhang, Haitao Lin, Xujun Zhang, Xiaorui Wang, Zhenxing Wu, Qing Ye, Weibo Zhao et al.
2025 -
12. MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields
Ilyes Batatia, Dávid Péter Kovács, Gregor N. C. Simm, Christoph Ortner, Gábor Csányi
2022-06-15 -
13. So3krates: Equivariant attention for interactions on arbitrary length-scales in molecular systems
J. Thorben Frank, Oliver T. Unke, Klaus-Robert Müller
2022-05-28 -
14. UMA: A Family of Universal Models for Atoms
Brandon M. Wood, Misko Dzamba, Xiang Fu, Meng Gao, Muhammed Shuaibi, Luis Barroso-Luque, Kareem Abdelmaqsoud et al.
2025-06-30 -
15. Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction
Xiang Fu, Brandon M. Wood, Luis Barroso-Luque, Daniel S. Levine, Meng Gao, Misko Dzamba, C. Lawrence Zitnick
2025-02-17 - Unknown
Chemistry
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1. Concerted proton-electron transfer reactions in the Marcus inverted region
Giovanny A. Parada, Zachary K. Goldsmith, Scott Kolmar, Belinda Pettersson Rimgard, Brandon Q. Mercado, Leif Hammarström, Sharon Hammes-Schiffer et al.
2019 - 2015
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3. Free Energies of Proton-Coupled Electron Transfer Reagents and Their Applications
Rishi G. Agarwal, Scott C. Coste, Benjamin D. Groff, Abigail M. Heuer, Hyunho Noh, Giovanny A. Parada, Catherine F. Wise et al.
2021 - 2025
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5. Mathematical Framework to Identify Optimal Molecule Based on Virtual Ligand Strategy
Wataru Matsuoka, Ken Hirose, Ren Yamada, Taihei Oki, Satoru Iwata, Satoshi Maeda
2025 - 2015
Deep Learning
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1. Attention Is All You Need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser et al.
2017-06-12 -
2. Masked Diffusion Language Models with Frequency-Informed Training
Despoina Kosmopoulou, Efthymios Georgiou, Vaggelis Dorovatas, Georgios Paraskevopoulos, Alexandros Potamianos
2025-09-05 - 2025-07-20
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4. A Convergence Theory for Diffusion Language Models: An Information-Theoretic Perspective
Gen Li, Changxiao Cai
2025-05-27 -
5. Mathematical Foundations of Geometric Deep Learning
Haitz Sáez de Ocáriz Borde, Michael Bronstein
2025-08-01
Generation and Exploration
- 2024-10-26
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2. Comprehensive Benchmark Study of Diffusion-Based 3D Molecular Generation Models
Yifei Qin, Xuexin Wei, Mingyuan Xu, Jiaqiang Wu, Miru Tang, Ting Ran, Hongming Chen
2025 -
3. A Genetic Algorithm for Navigating Synthesizable Molecular Spaces
Alston Lo, Connor W. Coley, Wojciech Matusik
2025-09-25 -
4. Guiding Diffusion Models with Reinforcement Learning for Stable Molecule Generation
Zhijian Zhou, Junyi An, Zongkai Liu, Yunfei Shi, Xuan Zhang, Fenglei Cao, Chao Qu et al.
2025-08-22 -
5. Scaffold Hopping with Generative Reinforcement Learning
Luke Rossen, Finton Sirockin, Nadine Schneider, Francesca Grisoni
2025 -
6. Controlled Generation with Equivariant Variational Flow Matching
Floor Eijkelboom, Heiko Zimmermann, Sharvaree Vadgama, Erik J Bekkers, Max Welling, Christian A. Naesseth, Jan-Willem van de Meent
2025-06-23 -
7. Equivariant Diffusion for Molecule Generation in 3D
Emiel Hoogeboom, Victor Garcia Satorras, Clément Vignac, Max Welling
2022-03-31 -
8. In Search of Beautiful Molecules: A Perspective on Generative Modeling for Drug Design
Remco L. van den Broek, Shivam Patel, Gerard J. P. van Westen, Willem Jespers, Woody Sherman
2025 -
9. ShEPhERD: Diffusing shape, electrostatics, and pharmacophores for bioisosteric drug design
Keir Adams, Kento Abeywardane, Jenna Fromer, Connor W. Coley
2024-10-22 -
10. GenMol: A Drug Discovery Generalist with Discrete Diffusion
Seul Lee, Karsten Kreis, Srimukh Prasad Veccham, Meng Liu, Danny Reidenbach, Yuxing Peng, Saee Paliwal et al.
2025-01-10
Retrosynthesis and Solubility
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1. Physics-Based Solubility Prediction for Organic Molecules
Daniel J. Fowles, Benedict J. Connaughton, James W. Carter, John B. O. Mitchell, David S. Palmer
2025 -
2. Data-driven organic solubility prediction at the limit of aleatoric uncertainty
Attia, Lucas, Burns, Jackson W., Doyle, Patrick S., Green et al.
2025-08-19 -
3. Temperature-Dependent Small-Molecule Solubility Prediction Using MoE-Enhanced Directed Message Passing Neural Networks
Lixiang Guo, Yujing Zhao, Qilei Liu, Lei Zhang, Jian Du, Qingwei Meng
2025 -
4. Enhancing Monte Carlo Tree Search for Retrosynthesis
Ton M. Blackshaw, Joseph C. Davies, Kristian T. Spoerer, Jonathan D. Hirst
2025 -
5. Rethinking Retrosynthesis: Curriculum Learning Reshapes Transformer-Based Small-Molecule Reaction Prediction
Rahul Sheshanarayana, Fengqi You
2025 -
6. Rethinking Molecule Synthesizability with Chain-of-Reaction
Seul Lee, Karsten Kreis, Srimukh Prasad Veccham, Meng Liu, Danny Reidenbach, Saee Paliwal, Weili Nie et al.
2025-09-19