Weekly BioML Digest [January 19, 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)

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

• Semantic design of functional de novo genes from a genomic language model
  Merchant, Aditi T., King, Samuel H., Nguyen, Eric, Hie, Brian L. — Nature, 2026-01-15
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• Language model-guided anticipation and discovery of mammalian metabolites
  Qiang, Hantao, Wang, Fei, Lu, Wenyun, Xing, Xi, Kim, Hahn, Mérette, Sandrine A. M., Ayres, Lucas B., Oler, Eponine, AbuSalim, Jenna E., Roichman, Asael, Neinast, Michael, Cordova, Ricardo A., Lee, Won Dong, Herbst, Ehud, Gupta, Vishu, Neff, Samuel L., Hiebert-Giesbrecht, Mickel, Young, Adamo, Gautam, Vasuk, Tian, Siyang, Wang, Bo, Röst, Hannes, Baidwan, Jatinder, Greiner, Russell, Chen, Li, Johnston, Chad W., Foster, Leonard J., Shapiro, Aaron M., Wishart, David S., Rabinowitz, Joshua D., Skinnider, Michael A. — Nature, 2026-01-14
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• Sequence-based generative AI design of versatile tryptophan synthases
  Lambert, Théophile, Tavakoli, Amin, Dharuman, Gautham, Yang, Jason, Bhethanabotla, Vignesh, Kaur, Sukhvinder, Hill, Matthew, Ramanathan, Arvind, Anandkumar, Anima, Arnold, Frances H. — Nature Communications, 2026-01-14
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• MetalloDock: Decoding Metalloprotein-Ligand Interactions via Physics-Aware Deep Learning for Metalloprotein Drug Discovery.
  Hui Zhang, Xujun Zhang, Qun Su, Yangyang Zheng, Linlong Jiang, Kai Zhu, Qiaolin Gou, Odin Zhang, Shi Li, Bo Peng, Shaokai Ni, Yushen Du, Jiayi Tang, Yu Kang, Chang-Yu Hsieh, Dan Li, Wenteng Chen, Tingjun Hou, P. Pan — Journal of the American Chemical Society, 2026-01-13
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• Predicting epistasis across proteins by structural logic.
  Michelle Tang, Gareth A. Cromie, Anowarul Kabir, Martin S Timour, Julee Ashmead, Russell S. Lo, Nathaniel Corley, Frank Dimaio, Hiroki Morizono, L. Caldovic, Nicholas Ah Mew, Andrea L. Gropman, Amarda Shehu, Aim E M Dudley — Proceedings of the National Academy of Sciences of the United States of America, 2026-01-16
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• Proteolysis-targeting Chimera efficacy prediction using a deep-learning–QSP model
  Goo, Sungwoo, Kim, Jina, Lee, Soyoung, Jung, Sangkeun, Chae, Jung-woo, Choi, Jae-mun, Yun, Hwi-yeol — Journal of Cheminformatics, 2026-01-12
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• ME-pKa: A Deep Learning Method with Multimodal Learning for Protein pKa Prediction.
  Shanshan Shi, Runyu Miao, Danlin Liu, Yiqing Zhang, Shanshan Ruan, Qian Xu, Jing Wang, Honglin Li, Shiliang Li — Journal of chemical theory and computation, 2026-01-13
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• Benchmarking co-folding methods to predict the structures of covalent protein–ligand complexes
  Zhang, Tong-han, Zhu, Jin-tao, Huang, Zhi-xian, Xie, Juan, Pei, Jian-feng, Lai, Lu-hua — Acta Pharmacologica Sinica, 2026-01-12
  abs

• Machine learning prediction of multiple distinct high-affinity chemotypes for α-synuclein fibrils.
  Xinning Li, Ryann M. Perez, Z. Tu, R. Mach, Sam Giannakoulias, E. Petersson — Chemical communications, 2026-01-13
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• Bream: an open-source deep learning framework for simultaneous base calling and DNA methylation detection on novel nanopore sequencing platforms
  Hui-Cong Yao, Bo Wu, Chen-Liang Ye, Xin Bai, He-Xu Chen, Geng Hu, Chuan-Le Xiao — Frontiers in Genetics, 2026-01-14
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• GraphSTAR: Proximal Operator-Based Graph Neural Network Enhanced by Dynamic Graph Aggregation for Spatial Transcriptomics.
  Junyu Li, Jingquan Yan, Yi Liao, Wenxiong Liao, Ye Liu, Hongmin Cai — IEEE journal of biomedical and health informatics, 2026-01-12
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• An AI-guided framework reveals conserved features governing microRNA strand selection
  Dalton Meadows, Hailee Hargis, Amanda Ellis, Heewook Lee, Marco Mangone — Nucleic Acids Research, 2026-01-14
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• RaptScore: a large language model-based algorithm for versatile aptamer evaluation
  Akira Kimura-Yamazaki, Tatsuo Adachi, Shigetaka Nakamura, Yoshikazu Nakamura, M. Hamada — Nucleic Acids Research, 2026-01-14
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• Multi-view deep learning of highly multiplexed imaging data improves association of cell states with clinical outcomes
  Shanza Ayub, Jennifer L. Gorman, Edward L Y Chen, Hartland W. Jackson, Alina Selega, Kieran R Campbell — Bioinformatics Advances, 2026-01-15
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• Predicting anti-PD-1 immune checkpoint blockade response in melanoma patients with spatially aware machine learning models
  Pybus, Alyssa, Kirchgaessner, Raphael, Nguyen, Jonathan, Moran Segura, Carlos, Morais Lyra, Paulo Cilas, Jr, Rose, Trevor, Gray, Jhanelle, Goecks, Jeremy, Markowitz, Joseph — npj Precision Oncology, 2026-01-12
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• EnsDTI: Predicting Drug-Target Interaction with Mixture-of-Experts and Confidence Assessment.
  Yijingxiu Lu, Soosung Kang, Sun Kim, Sangseon Lee — IEEE transactions on computational biology and bioinformatics, 2026-01-14
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• Drug-target Affinity Prediction Based on Graph Transformer and Selfattention Mechanism Kinase-specific Drug-target Affinity Prediction with Graph Transformer and Self-Attention Fusion.
  Shiqian Han, Jiahao Shi, Jun Wang — Current computer-aided drug design, 2026-01-16
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• ONCOPLEX: an oncology-inspired hypergraph model integrating diverse biological knowledge for cancer driver gene prediction
  Alotaibi, Etab Mohammed, Alkhnbashi, Omer S., Tran, Van Dinh — Scientific Reports, 2026-01-13
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• Self-supervised learning on graphs predicts non-coding RNA and disease associations
  Wu, Qingwen, Tang, Sujuan — Scientific Reports, 2026-01-14
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• BOLD-GPCRs: A Transformer-Powered App for Predicting Ligand Bioactivity and Mutational Effects across Class A GPCRs.
  D. Provasi, Kirill Konovalov, Nicholas Riina, Olivia Cullen, M. Filizola — Journal of chemical information and modeling, 2026-01-14
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🧬 Section 2: Preprints (arXiv + bioRxiv)

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

• 🧬 Designing AI-programmable therapeutics with the EDEN family of foundation models
  Munsamy, G.; Ayres, G.; Greco, C.; Kam, K.; Minto-Cowcher, G.; St John, J.; Bohnuud, T.; Bakalar, M.; Chow, W.; Pecoraro, R.; D.T. Torres, M.; Kollasch, A.; Leung, M.; Sirelkhatim, H.; Farina, F.; McGinnis, C.; Sridhar, S.; Anderson, D.; Oteri, F.; Takhibakhshi, A.; Dona, J.; Shimko, T.; Steenbeke, C.; Papadopoulos, A.; Krolick, M.; Spoendlin, F.; Gupta, P.; Kumar, S.; Bara, A.; Wilber, J.; Ferruz, N.; Rvachoc, T.; Wang, F.; Cao, H.; Lee, H.-S.; Mehta, J.; Chaleil, R.; Pereno, V.; Potti, S.; Emerson, C.; Dew, R. T.; Yang, K. K.; Nguyen, E.; Tadimeti, N.; Banfield, J.; Frame, A.; Bolton, E.; Ru — bioRxiv, 2026-01-12
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• 🧬 Origin-1: a generative AI platform for de novo antibody design against novel epitopes
  Levine, S.; King, J. E.; Stern, J.; Grayson, D.; Wang, R.; Yin, R.; Lupo, U.; Kulyte, P.; Brand, R. M.; Bertin, T.; Pfingsten, R.; Cejovic, J.; Chung, C.; Luton, B. K.; Hagemann, A.; Haile, R.; Medina, E.; Panwar, P.; Dubrovskyi, O.; LaCombe, C.; Anderson, Z.; Mildh, D.; Benjamin, S.; Kaiser, J.; Ferron, J.; Sarrico, M.; Kershner, A.; Mishra, A.; Ejan, K. R.; Marsh, E. K.; Bringas, P.; Vilaychack, P.; Chapman, K.; Ripley, J.; Gowda, M.; Collins, K. M.; McCloskey, C. M.; Joseph, J. S.; Ripley, R.; Abdulhaqq, S. A.; Feltner, A.; Guerin, M.; Goby, J.; Hendricks, J.; Castillo, D.; McClain, S.; Gan — bioRxiv, 2026-01-14
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• 🧬 HalluDesign: Protein Optimization and de novo Design via Iterative Structure Hallucination and Sequence Design
  Fang, M.; Wang, C.; Shi, J.; Lian, F.; Jin, Q.; Wang, Z.; Zhang, Y.; Chen, P.; Cui, Z.; Wang, Y.; Zhang, Z.; Ke, Y.; Han, Q.; Cao, L. — bioRxiv, 2026-01-16
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• 🧬 Designing AAV Capsid Protein with viability-guided Diffusion Model
  Xiao, S.; Zeng, X.; Jiao, S.; Lu, D.; Xie, D.; Liu, J.; Peng, J. — bioRxiv, 2026-01-12
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• 🧬 PathDiffusion: modeling protein folding pathway using evolution-guided diffusion
  Zhao, K.; Xiang, C.; Cheng, B.; Shen, Y.; Wang, W.; Chen, S.; Su, B.; Zhang, G.; Peng, Z.; Yang, J. — bioRxiv, 2026-01-16
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• 🧬 Extending Conformational Ensemble Prediction to Multidomain Proteins and Protein Complex
  Zhu, J.; Bülow, S. v.; Liu, H.; Lindorff-Larsen, K.; Chen, H. — bioRxiv, 2026-01-15
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• 📄 Contrastive Geometric Learning Unlocks Unified Structure- and Ligand-Based Drug Design
  Lisa Schneckenreiter, Sohvi Luukkonen, Lukas Friedrich, Daniel Kuhn, Günter Klambauer — arXiv, 2026-01-14
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• 🧬 SynGlue: AI-Driven Designer for Clinically Actionable Multi-Target Therapeutics
  Solanki, S.; Mohanty, S. K.; Satija, S.; Chauhan, S.; Bandaru, N. V. M. R.; Dukare, S.; Tiwari, N. K.; R, N. K.; B, A. A.; Mukherjee, S.; Chikkanna, D.; Balasubramanian, W. R.; Sammeta, S. R.; Gautam, V.; Arora, S.; Kumar, S.; Duari, S.; Sharma, A.; Shome, R.; Sengupta, D.; Abbineni, C.; Samajdar, S.; Ahuja, G. — bioRxiv, 2026-01-18
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• 🧬 Advancing Protein Design via Multi-Agent Reinforcement Learning with Pareto-Based Collaborative Optimization
  Zhu, M.; Rao, J.; Chen, X.; Yuan, Q.; Yang, Y. — bioRxiv, 2026-01-14
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• 📄 PLANET v2.0: A comprehensive Protein-Ligand Affinity Prediction Model Based on Mixture Density Network
  Haotian Gao, Xiangying Zhang, Jingyuan Li, Xinchong Chen, Haojie Wang, Yifei Qi, Renxiao Wang — arXiv, 2026-01-12
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• 📄 Enhancing Spatial Reasoning in Large Language Models for Metal-Organic Frameworks Structure Prediction
  Mianzhi Pan, JianFei Li, Peishuo Liu, Botian Wang, Yawen Ouyang, Yiming Rong, Hao Zhou, Jianbing Zhang — arXiv, 2026-01-14
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• 📄 Breaking the Bottlenecks: Scalable Diffusion Models for 3D Molecular Generation
  Adrita Das, Peiran Jiang, Dantong Zhu, Barnabas Poczos, Jose Lugo-Martinez — arXiv, 2026-01-13
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• 🧬 SE3Bind: SE(3)-equivariant model for antibody-antigen binding affinity prediction
  Subedy, A.; Bhadra-Lobo, S.; Lamoureux, G. — bioRxiv, 2026-01-18
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• 🧬 STRUCT2SEQ: RNA INVERSE FOLDING WITH DEEP Q-LEARNING
  He, S.; Sun, Q. — bioRxiv, 2026-01-18
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• 🧬 TcrDesign: De novo design of epitope specific full-length T cell receptors
  Diao, K.; Chen, J.; Zhao, X.; Wu, T.; Qiu, D.; Wang, W.; Wang, H.; Liu, X.-S. — bioRxiv, 2026-01-16
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• 🧬 Beyond native sequence recovery: Improved modeling of thesequence-energy landscape of protein structures
  Birnbaum, F.; Keating, A. E. — bioRxiv, 2026-01-15
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• 🧬 HERMES: Holographic Equivariant neuRal network model for Mutational Effect and Stability prediction
  Visani, G. M.; Jones, Z.; Galvin, W.; Pun, M. N.; Daniel, E.; Borisiak, K.; Wagura, U.; Nourmohammad, A. — bioRxiv, 2026-01-15
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• 🧬 Explaining how mutations affect AlphaFold predictions
  Clore, M. F.; Thole, J. F.; Dontha, S.; Sharma, P.; Greenberg, N.; Strub, M.-P.; Starich, M.; Jensen, D.; Volkman, B.; Coudron, M.; Porter, L. — bioRxiv, 2026-01-12
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• 🧬 Leveraging Unified Sequence-Structure Representations for Enhanced Protein Stability Prediction
  Ahmed, Y.; Mahmoud, K.; Salah, O. — bioRxiv, 2026-01-16
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• 🧬 Protein-peptide Interaction Representation Learning with Pretrained Language Models
  Zhan, X.; Zhai, S.; Liu, T.; Lin, S.; Bi, T.; Zhu, B.; Siu, S. W. I. — bioRxiv, 2026-01-13
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