Weekly BioML Digest [January 26, 2026]

Machine Learning × Computational Biology paper compilation

Hey! It's your weekly digest of machine learning papers in CompBio and Drug Discovery.

Feedback? Email me at biomldigest@gmail.com.

📚 Section 1: Peer-Reviewed Journals (Top 20)

400 papers matched filters → 20 selected after LLM relevance + novelty ranking.

• PropMolFlow: property-guided molecule generation with geometry-complete flow matching
  Zeng, Cheng, Jin, Jirui, Ambrose, Connor, Karypis, George, Transtrum, Mark, Tadmor, Ellad B., Hennig, Richard G., Roitberg, Adrian, Martiniani, Stefano, Liu, Mingjie — Nature Computational Science, 2026-01-21
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• Protein folding stability estimation with explicit consideration of unfolded states
  Lee, Heechan, Cho, Yugyeong, Yun, Jeongwon, Steinegger, Martin, Kim, Ho Min, Park, Hahnbeom — Nature Communications, 2026-01-21
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• Enhancing k_cat prediction through residue-aware attention mechanism and pre-trained representations
  Cai, Yunxiang, Ge, Fengya, Zhang, Chuanlei, Chen, Hao, Qi, Xuan, Liao, Xiaoping, Wang, Lin — Communications Biology, 2026-01-22
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• CLAMP: predicting specific protein-mediated chromatin loops in diverse species with a chromatin accessibility language model
  He, Zhijie, Sun, Yu, Li, Hao, Sun, Canzhuang, Yang, Xianhui, Chen, Hebing, Liao, Mingzhi, Bo, Xiaochen — Genome Biology, 2026-01-19
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• The role of low-complexity repeats in RNA–RNA interactions and a deep learning framework for duplex prediction
  Setti, Adriano, Bini, Giorgio, Flaminia, Pellegrini, Maiorca, Valentino, Proietti, Gabriele, Vrachnos, Dimitrios-Miltiadis, D’Angelo, Angelo, Armaos, Alexandros, Martone, Julie, Monti, Michele, Ruocco, Giancarlo, Rodolà, Emanuele, Bozzoni, Irene, Colantoni, Alessio, Tartaglia, Gian Gaetano — Nature Communications, 2026-01-23
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• CompBind: Complex Guided Pretraining-Based Structure-Free Protein-Ligand Affinity Prediction.
  Duoyun Yi, Yanpeng Zhao, Huiyan Xu, Yixin Zhang, Mengxuan Wan, Peng Zan, Song He, Xiaochen Bo — Journal of chemical information and modeling, 2026-01-21
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• Geometry-Enhanced Multiscale Joint Representation Learning for Drug-Target Interaction Prediction.
  Qiao Ning, S. Qiao, Yawen Cai, Yanpeng Liu, Hui Li, Qian Ma, Shikai Guo — Journal of chemical information and modeling, 2026-01-20
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• MolCluster: An Unsupervised Framework for Multiscale Molecular Representations with Physically Consistent Resolution Control.
  Zhixuan Zhong, Linbo Ma, Jian Jiang — Journal of chemical theory and computation, 2026-01-21
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• CMD‐FEP: Machine‐Learned Free‐Energy Prediction for Efficient Screening of Material Interfacial Binder
  Mengxia Mo, Haiyang Yu, Xing Zhou, Wangdong Yang, Can Leng, Wei Zhou, Wei‐Qing Huang, Chengkun Wu, Jie Liu — Advanced Functional Materials, 2026-01-20
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• Physics-informed deep learning enables fast ultrahigh-resolution nuclear magnetic resonance spectroscopy
  Bao, Jianfeng, Ni, Yang, Hu, Liangliang, Zhan, Haolin — Communications Chemistry, 2026-01-23
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• ProteoAutoNet: high-throughput co-eluted protein analysis with robotics and machine learning
  Lyu, Mengge, Hu, Pingping, Zhang, Guangmei, Ma, Kunpeng, Zhang, Xuedong, Liu, Pu, Zhang, Sai, Li, Xiangqing, Sun, Rui, Chen, Yi, Guo, Tiannan — Nature Communications, 2026-01-22
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• Large-scale serum N-glycomics tracks N-glycosylation dynamics in hepatocellular carcinoma progression and enables early diagnosis
  Fu, Bin, Chen, Jierong, Liu, Xuejiao, Li, Jiale, Feng, Xisong, Wang, Jun, Li, Yueyue, Zhang, Yuxiao, Ma, Jianhong, Wang, Yunxia, Gu, Bing, Zhang, Ying, Lu, Haojie — Nature Communications, 2026-01-20
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• Proteomic profiling of single extracellular vesicles as a promising new approach for the diagnosis and treatment modality of advanced ovarian cancer
  Wu, Beier, Yang, Xuping, Cai, Yanling, Wan, Shihan, Liu, Jingfang, Xing, Jie, Chen, Xin, Zhang, Jiejie, Jin, Yanlu, Yu, Aijun, Yang, Li — npj Precision Oncology, 2026-01-21
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• Multi-strategy embedded framework for neoantigen vaccine maturation
  Zhang, Guanqiao, Fu, Yaqi, Chan, Kevin C., Jin, Ruofan, Yang, Yuxuan, Fu, Lei, Zhou, Ruhong — Scientific Reports, 2026-01-19
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• Making large language models reliable data science programming copilots for biomedical research
  Wang, Zifeng, Danek, Benjamin, Yang, Ziwei, Chen, Zheng, Sun, Jimeng — Nature Biomedical Engineering, 2026-01-22
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• Mimicking opioid analgesia in cortical pain circuits
  Oswell, Corinna S., Rogers, Sophie A., James, Justin G., McCall, Nora M., Hsu, Alex I., Salimando, Gregory J., Mahmood, Malaika, Wooldridge, Lisa M., Wachira, Meghan, Jo, Adrienne Y., Sandoval Ortega, Raquel Adaia, Wojick, Jessica A., Beattie, Katherine, Farinas, Sofia A., Chehimi, Samar N., Rodrigues, Amrith, Wu, Jacqueline W. K., Ejoh, Lindsay L., Kimmey, Blake A., Lo, Emily, Azouz, Ghalia, Vasquez, Jose J., Banghart, Matthew R., Beier, Kevin T., Creasy, Kate Townsend, Crist, Richard C., Ramakrishnan, Charu, Reiner, Benjamin C., Deisseroth, Karl, Yttri, Eric A., Corder, Gregory — Nature, 2026-01-22
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• Predatory aggression evolved through adaptations to noradrenergic circuits
  Eren, Güniz Göze, Böger, Leonard, Roca, Marianne, Hiramatsu, Fumie, Liu, Jun, Alvarez, Luis, Goetting, Desiree L., Cockram, Lewis A., Zorn, Nurit, Han, Ziduan, Okumura, Misako, Scholz, Monika, Lightfoot, James W. — Nature, 2026-01-21
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• Peptide turnover prediction using transformer architectures on large-scale time-series proteomic data
  Ishino, Koki, Yoshizawa, Akiyasu C., Liu, Yuting, Okuda, Shujiro — BMC Genomics, 2026-01-22
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• Interpretable Multimodal Molecular Language Model for Drug-Target Interaction Prediction
  Yu, Hui, Wang, Qingyong, Zhou, Xiaobo, Gu, Lichuan — Interdisciplinary Sciences: Computational Life Sciences, 2026-01-22
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• Domain adaptation, self-supervision, and generative augmentation enhance GNNs for breast cancer prediction
  Qiu, Shi, Zhao, Yun, Li, Xiuchang — Scientific Reports, 2026-01-20
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🧬 Section 2: Preprints (arXiv + bioRxiv)

114 papers matched filters → 20 selected after LLM relevance + novelty ranking.

• 🧬 High-Affinity Protein Binder Design via Flow Matching and In Silico Maturation
  Yu, Q.; Guo, L.; Qin, X.; Huang, X.; Tian, B.; Wang, H.; Liu, Y.; Lang, Y.; Wang, D.; Shen, Z.; Lin, J.; Chen, M. — bioRxiv, 2026-01-22
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• 🧬 Neural Spectral Prediction for Structure Elucidation with Tandem Mass Spectrometry
  Wang, R.; Manjrekar, M.; Mahjour, B.; Avila-Pacheco, J.; Provenzano, J.; Reynolds, E.; Lederbauer, M.; Mashin, E.; Goldman, S. L.; Wang, M.; Weng, J.-K.; Plata, D. L.; Clish, C. B.; Coley, C. W. — bioRxiv, 2026-01-22
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• 🧬 FoldVision: A compute-efficient atom-level 3D protein encoder
  Kroll, A.; Yadav, S.; Lercher, M. — bioRxiv, 2026-01-25
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• 🧬 Separating selection from mutation in antibody language models
  Matsen, F. A.; Dumm, W.; Sung, K.; Johnson, M. M.; Rich, D.; Starr, T.; Song, Y. S.; Fukuyama, J.; Haddox, H. K. — bioRxiv, 2026-01-25
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• 🧬 Generative modeling reveals the connection between cellular morphology and gene expression
  Wen, S.; Vinas, R.; Bues, J.; Lambert, C.; Grenningloh, N.; Ferrari, T.; Bugani, E.; Pezoldt, J.; Love, J.; Karthaus, W.; Deplancke, B.; Brbic, M. — bioRxiv, 2026-01-24
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• 🧬 STELLA: Self-Evolving LLM Agent for Biomedical Research
  Jin, R.; Xu, M.; Meng, F.; Wan, G.; Cai, Q.; Jiang, Y.; Han, J.; Chen, Y.; Lu, W.; Wang, M.; Lan, Z.; Jiang, Y.; Liu, J.; Wang, D.; Cong, L.; Zhang, Z. — bioRxiv, 2026-01-25
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• 📄 Multi-objective fluorescent molecule design with a data-physics dual-driven generative framework
  Yanheng Li, Zhichen Pu, Lijiang Yang, Zehao Zhou, Yi Qin Gao — arXiv, 2026-01-20
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• 🧬 CellFluxV2: An Image Generative Foundation Model for Virtual Cell Modeling
  Zhang, Y.; Su, Y.; Wefers, Z.; Su, S.; Li, H.; Li, T.; Wang, C.; Burgess, J.; Lozano, A.; Zhou, L.; Ding, D.; Nirschl, J.; Lundberg, E.; Yeung-Levy, S. — bioRxiv, 2026-01-20
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• 🧬 Spatial Decoding of Tertiary Lymphoid Structure Maturation in Non-Small Cell Lung Cancer Using Deep Neural Networks
  Chen, B.; Foo, C.; Andersson, A.; Kayser, B. D.; Yang, Y.; Missarova, A.; Kulkarni, P.; Huetter, J.-C.; Chatterjee, S.; Kim, Y.; Liang, Y.; Killinbeck, E.; Murphy, S.; Fuentes, E.; Giltnane, J. M.; Jesudason, R.; Li, B.; BenTaieb, A.; Richmond, D.; Biancalani, T.; Johnston, R. J.; Lubeck, E.; Risom, T.; Rozenblatt-Rosen, O.; Wei, R.; McGinnis, L. M. — bioRxiv, 2026-01-20
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• 🧬 Steering Conformational Sampling in Boltz-2 via Pair Representation Scaling
  Suzuki, S.; Amagasa, T. — bioRxiv, 2026-01-23
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• 🧬 TCRAD: An End-to-End Framework for Antigen-Targeted T Cell Receptor Design
  Li, C.; Guo, Y.; Guan, X.; Chen, H.; Zhang, Y.; Yang, P.; Lou, J. — bioRxiv, 2026-01-23
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• 🧬 Beyond native sequence recovery: Improved modeling of thesequence-energy landscape of protein structures
  Birnbaum, F.; Keating, A. E. — bioRxiv, 2026-01-20
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• 🧬 Rewriting protein alphabets with language models
  Pantolini, L.; Studer, G.; Engist, L.; Pudziuvelyte, I.; Pommerening, F.; Waterhouse, A. M.; Tauriello, G.; Steinegger, M.; Schwede, T.; Durairaj, J. — bioRxiv, 2026-01-19
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• 📄 Generating Cyclic Conformers with Flow Matching in Cremer-Pople Coordinates
  Luca Schaufelberger, Aline Hartgers, Kjell Jorner — arXiv, 2026-01-19
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• 🧬 Optimizing broadly neutralizing antibodies via all-atom interaction modeling and pre-trained language models
  Song, Y.; Wu, F.; Wang, R.; He, B.; Yan, Q.; Huang, X.; Chen, S.; Yuan, Q.; Rao, J.; Tang, Z.; He, H.; Zhao, J.; Yang, Y.; Yao, J. — bioRxiv, 2026-01-21
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• 🧬 Ancestral sequence reconstruction using generative models
  Dotan, E.; Wygoda, E.; Schers, A.; Lyubman, I.; Belinkov, Y.; Pupko, T. — bioRxiv, 2026-01-21
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• 🧬 Benchmarking generative scaffold design methods for peptide engineering in TCR-MHC complexes
  Xie, L.; Dam, G.-B.; Patel, Y.; Denzler, L.; Shao, Y.; Wang, R.; Caron, E.; Yasumizu, Y.; Hafler, D. A.; Rodriguez Martinez, M. — bioRxiv, 2026-01-23
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• 🧬 FrustraMPNN: An ultra-fast deep learning tool for proteome-scale analysis of deep mutational single-residue local energetic frustration in proteins
  Beining, M.; Aliaga, F. E.; Parra, R. G.; Schoeder, C. T.; Ramirez-Sarmiento, C. A.; Meiler, J. — bioRxiv, 2026-01-23
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• 📄 3D Molecule Generation from Rigid Motifs via SE(3) Flows
  Roman Poletukhin, Marcel Kollovieh, Eike Eberhard, Stephan Günnemann — arXiv, 2026-01-23
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• 🧬 MoDPA: Inferring Modification-dependent Protein Associations from Uniformly Reprocessed Mass Spectrometry
  Massignani, E.; Tichshenko, N.; Velghe, K.; Martens, L. — bioRxiv, 2026-01-21
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