Weekly BioML Digest [November 17, 2025]

Machine Learning × Computational Biology compilation from arXiv + bioRxiv

Hey! It's your weekly automated digest of machine learning papers in CompBio and Drug Discovery. Here is how it was created:

• Both arXiv and bioRxiv queried for new papers published in the past week [November 10, 2025 - November 16, 2025].
• Found 4846 new arXiv papers and 1439 new bioRxiv papers.
• 25 arXiv papers and 89 bioRxiv papers matched keyword filters.
• 30 papers are included in this digest after deduplication and ChatGPT relevance+novelty reranking.

PS. I'm working on adding papers from other journals like Nature, PNAS, etc. to this list. Stay tuned and let me know your thoughts or suggestions at biomldigest@gmail.com.

Here are your top 30 papers:

• 🧬 A unified language model bridging de novo and fragment-based 3D molecule design delivers potent CBL-B inhibitors for cancer treatment
  Wang, H.; Sun, G.; Zhang, B.; Wang, Y.; Xi, B.; Yang, M.; Liu, C.; Ge, Y.; Fan, F.; Feng, W.; Zhu, Y.; Xiao, Y.; Wang, Y.; Liu, Z.; Jiang, D.; Wang, H.; Zhou, W.; Huang, B. — bioRxiv:bioinformatics, 2025-11-14
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• 🧬 CDR Conformation Aware Antibody Sequence Design with ConformAb
  Sinha, I.; Stanton, S.; Lillington, S.; Robinson, S. A.; Nerli, S.; Zadorozhny, K.; Kleinhenz, J.; MohammadiPeyhani, H.; Dillon, M.; Chen, Y.; Bevers, J.; Wu, Y.; Watkins, A. M.; Dwyer, H.; Bonneau, R.; Cho, K.; Seeger, F.; Gligorijevic, V.; Kelow, S. — bioRxiv:biophysics, 2025-11-13
  abs · pdf

• 🧬 E1: Retrieval-Augmented Protein Encoder Models
  Jain, S.; Beazer, J.; Ruffolo, J. A.; Bhatnagar, A.; Madani, A. — bioRxiv:synthetic biology, 2025-11-13
  abs · pdf

• 🧬 Phylogeny-agnostic strain-level prediction of phage-host interactions from genomes
  Noonan, A. J. C.; Moriniere, L. C.; Rivera-Lopez, E. O.; Patel, K.; Pena, M.; Svab, M.; Kazakov, A.; Deutschbauer, A.; Dudley, E. G.; Mutalik, V. K.; Arkin, A. P. — bioRxiv:microbiology, 2025-11-15
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• 🧬 Multimodal AI Decodes Extreme Environment Functional Dark Matter Beyond Homology
  Xu, M.; Wang, D.; Liu, Q.; Jiang, H.; Liu, X.; Li, Y.; Wang, D.; Dong, H.; Yan, X.; Liu, Y.; Xu, A.; Peng, H.; Zhang, Y.; Li, H.; Li, S.; Chen, J.; Wu, X.; Wang, Y.; Li, D.; Liu, S.; Meng, L.; Li, Y.; Xue, C.; Jiang, L.; Zhang, Y.; Song, J.; Wang, M.; Guo, Y.; Li, Z.; Shen, Y.; Fu, X.; Mock, T.; Zhuang, Y.; Xue, C.; Wang, J.; Yang, H.; Xu, X.; Lee, S. M. Y.; Fan, G.; Mao, X. — bioRxiv:bioinformatics, 2025-11-15
  abs · pdf

• 🧬 SpaTranslator: A deep generative framework for universal spatial multi-omics cross-modality translation
  Dong, H.; Mao, S.; Liu, Y.; Tian, T.; Zhang, L.; Wu, J.; Zhang, S.; Jiang, P.; Yin, D.; Xing, X.; Wang, P.; Li, H. — bioRxiv:bioinformatics, 2025-11-16
  abs · pdf

• 🧬 SpecLig: Energy-Guided Hierarchical Model for Target-Specific 3D Ligand Design
  Zhang, P.; Han, R.; Kong, X.; Chen, T.; Ma, J. — bioRxiv:bioinformatics, 2025-11-13
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• 🧬 HyperBind2: Multi-Shot Learning Enables Progressive Improvement in Computational Antibody Discovery
  Dell'uomo, D.; Satz, A.; Averso, B. — bioRxiv:bioengineering, 2025-11-11
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• 📄 EPO: Diverse and Realistic Protein Ensemble Generation via Energy Preference Optimization
  Yuancheng Sun, Yuxuan Ren, Zhaoming Chen, Xu Han, Kang Liu, Qiwei Ye — cs.LG, 2025-11-13
  abs · pdf

• 📄 Branching Flows: Discrete, Continuous, and Manifold Flow Matching with Splits and Deletions
  Hedwig Nora Nordlinder, Lukas Billera, Jack Collier Ryder, Anton Oresten, Aron Stålmarck, Theodor Mosetti Björk, Ben Murrell — stat.ML, 2025-11-12
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• 📄 Controllable protein design through Feynman-Kac steering
  Erik Hartman, Jonas Wallin, Johan Malmström, Jimmy Olsson — cs.LG, 2025-11-12
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• 🧬 Learning the Unseen: Data-Augmented Deep Learning for PTM Discovery with Prosit-PTM
  Gabriel, W.; Zolg, D. P.; Giurcoiu, V.; Shouman, O.; Prokofeva, P.; Seefried, F.; Bayer, F. P.; Lautenbacher, L.; Soleymaniniya, A.; Schnatbaum, K.; Zerweck, J.; Knaute, T.; Delanghe, B.; Huhmer, A.; Wenschuh, H.; Reimer, U.; Medard, G.; Kuster, B.; Wilhelm, M. — bioRxiv:bioinformatics, 2025-11-12
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• 📄 Pretrained Joint Predictions for Scalable Batch Bayesian Optimization of Molecular Designs
  Miles Wang-Henderson, Benjamin Kaufman, Edward Williams, Ryan Pederson, Matteo Rossi, Owen Howell, Carl Underkoffler, Narbe Mardirossian, John Parkhill — cs.LG, 2025-11-13
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• 🧬 Characterizing spatial functional microniches with SpaceTravLR
  Ramjattun, K.; Wang, A.; Lee, H.; Giri, S.; Chen, Y.; MacDonald, W. A.; Lord, N.; Poholek, A. C.; Lee, Y.; Das, J. — bioRxiv:systems biology, 2025-11-14
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• 🧬 Deep learning the cis-regulatory code of chromatin dynamics during cellular reprogramming
  Nair, S.; Ameen, M.; Sundaram, L.; Pampari, A.; Schreiber, J.; Balsubramani, A.; Wang, Y. X.; Burns, D.; Blau, H. M.; Karakikes, I.; Wang, K. C.; Kundaje, A. — bioRxiv:genetics, 2025-11-11
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• 🧬 Genolator: A Multimodal Large Language Model Fusing Natural Language, Genomic, and Structural Tokens for Protein Function Interpretation
  Danner, M.; Islam, T.; Begemann, M.; Kraft, F.; Elbracht, M.; Kurth, I.; Krause, J. — bioRxiv:bioinformatics, 2025-11-14
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• 📄 SiDGen: Structure-informed Diffusion for Generative modeling of Ligands for Proteins
  Samyak Sanghvi, Nishant Ranjan, Tarak Karmakar — cs.LG, 2025-11-12
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• 🧬 Benchmarking Real-World Applicability of Molecular Generative Models from De novo Design to Lead Optimization with MolGenBench
  Cao, D.; Fan, Z.; Yu, J.; Chen, M.; Jiang, X.; Wang, X.; Sheng, X.; Zeng, C.; Luo, X.; Teng, D.; Zheng, M. — bioRxiv:bioinformatics, 2025-11-13
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• 🧬 TEIP: A Compact, Open-Source Framework for Predicting Tumor Epitope Immunogenicity in Glioblastoma Cancer Using Deep Learning and Multi-Modal Biological Features
  Shekhar, A. — bioRxiv:cancer biology, 2025-11-11
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• 🧬 USP-ddG: A Unified Structural Paradigm with Data Efficacy and Mixture-of-Experts for Predicting Mutational Effects on Protein-Protein Interactions
  Yu, G.; bi, x.; zhao, q.; Wang, J. — bioRxiv:molecular biology, 2025-11-11
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• 🧬 Integrating Millions of Years of Evolutionary Information into Protein Structure Models for Function Prediction
  Ma, R.; He, C.; Zhang, Z.; Zheng, H.; Duan, L. — bioRxiv:bioinformatics, 2025-11-13
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• 🧬 Chemical Dice Integrator (CDI): A Scalable Framework for Multimodal Molecular Representation Learning
  Ahuja, G.; Kumar, S.; Solanki, S.; Gupta, M.; Mohanty, S. K.; Satija, S.; Chauhan, S.; Duari, S.; Sharma, A.; Gautam, V.; Arora, S.; Shome, R.; Sinha, S.; Sharma, A. K.; Mittal, A.; Sengupta, D.; Murugan, N. A. — bioRxiv:bioinformatics, 2025-11-13
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• 🧬 Off-target mapping enhances selectivity of machine learning-predicted CK2 inhibitors
  Ying, H.; Kong, W.; Schulman, A.; Panajotovikj, N.; Tanoli, Z.; Mestres, J.; Aittokallio, T.; Miihkinen, M. — bioRxiv:pharmacology and toxicology, 2025-11-13
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• 🧬 Accelerating ligand discovery by combining Bayesian optimization with MMGBSA-based binding affinity calculations
  Andersen, L.; Rausch-Dupont, M.; Martinez Leon, A.; Volkamer, A.; Hub, J.; Klakow, D. — bioRxiv:bioinformatics, 2025-11-13
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• 🧬 A machine learning framework for supervised treatment response prediction from tumor transcriptomics: A large-scale pan-cancer study
  Pal, L. R.; Gertz, E. M.; Ulhas Nair, N.; Mukherjee, S.; Patiyal, S.; Cantore, T.; Campagnolo, E. M.; Chang, T.; Dhruba, S. R.; Kim, Y.; Shulman, E. D.; Rajagopal, P. S.; Hoang, D.-T.; Schaffer, A. A.; Ruppin, E. — bioRxiv:cancer biology, 2025-11-14
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• 🧬 A high-throughput platform for biophysical antibody developability assessment to enable AI/ML model training
  Arsiwala, A.; Bhatt, R.; van Niekerk, L.; Quintero Cadena, P.; Ao, X.; Rosenbaum, A.; Bhatt, A.; Smith, A.; Yang, Y.; Anderson, K.; Grippo, L.; Cao, X.; Cohen, R.; Patel, J.; Allen, O.; Faraj, A.; Nandy, A.; Hocking, J.; Tural, B.; Salvador, S.; Jacobowitz, J.; Schaven, K.; Sherman, M.; Shah, S.; Tessier, P. M.; Borhani, D. — bioRxiv:biophysics, 2025-11-14
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• 🧬 Predicting microbial transcriptome using annotated genome sequence
  FU, G.; YAN, Y.; ZHAO, Z.; CHEN, Y.; SHAO, B. — bioRxiv:bioinformatics, 2025-11-11
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• 🧬 Single-Cell Resolution of Cellular Damage Illuminates Disease Progression
  Padvitski, T.; Unger Avila, P.; Chen, H.; Oezel, C.; Braun, F.; Mueller, R.-U.; Westermark, P.; Brinkkoetter, P.; Schermer, B.; Benzing, T.; Wunderlich, T.; Kann, M.; Beyer, A.; Kroll, M.-K.; Vossen, C.; Wienand, P.; Butt, L.; Unnersjoe-Jess, D.; Goebel, H.; Kuppe, C. — bioRxiv:bioinformatics, 2025-11-12
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• 🧬 Uncovering the dark transcriptome in polarized neuronal compartments with mcDETECT
  Yuan, C.; Patel, K.; Shi, H.; Wang, H.-L.; Wang, F.; Li, R.; Li, Y.; Corces, V.; Shi, H.; Das, S.; Yu, J.; Jin, P.; Yao, B.; Hu, J. — bioRxiv:bioinformatics, 2025-11-12
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• 🧬 AstraBIND: Graph Attention Network for Predicting Ligand Binding Sites
  Goteti, A.; Bozkurt, C.; Vasilyeva, A. — bioRxiv:bioinformatics, 2025-11-11
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