Weekly BioML Digest [February 23, 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)

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

• Rapid directed evolution guided by protein language models and epistatic interactions.
  Vincent Q Tran, Matthew Nemeth, Liam J. Bartie, Sita S. Chandrasekaran, Alison Fanton, Hyungseok C. Moon, Brian L. Hie, Silvana Konermann, Patrick D. Hsu — Science, 2026-02-19
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• FATE-MAP predicts teratogenicity and human gastrulation failure modes by integrating deep learning and mechanistic modeling
  Rufo, Joseph, Qiu, Chongxu, Han, Dasol, Baxter, Naomi, Daley, Gabrielle, Dhillon, Jasmine, Wong, Felix, Collins, James J., Wilson, Maxwell Z. — Nature Communications, 2026-02-19
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• Accurate predictions of disordered protein ensembles with STARLING
  Novak, Borna, Lotthammer, Jeffrey M., Emenecker, Ryan J., Holehouse, Alex S. — Nature, 2026-02-18
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• DRfold2 is a deep learning-based tool that enables efficient and accurate RNA structure prediction.
  Yang Li, Chenjie Feng, Xi Zhang, Sho Tsukiyama, Duanyu Feng, Yang Zhang — PLoS biology, 2026-02-17
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• Learning the committor without collective variables
  Contreras Arredondo, Sergio, Tang, Chenyu, Talmazan, Radu A., Megías, Alberto, Chen, Cheng Giuseppe, Chipot, Christophe — Nature Computational Science, 2026-02-17
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• Extending the range of graph neural networks with global encodings
  Caruso, Alessandro, Venturin, Jacopo, Giambagli, Lorenzo, Rolando, Edoardo, El-Machachi, Zakariya, Noé, Frank, Clementi, Cecilia — Nature Communications, 2026-02-18
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• AI-guided competitive docking for virtual screening and compound efficacy prediction
  Mirgaux, Manon, Barcelli, Valeria, Chua, Adeline C. Y., Bifani, Pablo, Wintjens, René — npj Drug Discovery, 2026-02-16
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• From single-sequences to evolutionary trajectories: protein language models capture the evolutionary potential of SARS-CoV-2
  Lamb, Kieran D., Hughes, Joseph, Lytras, Spyros, Young, Francesca, Koci, Orges, Herzig, James C., Lovell, Simon C., Grove, Joe, Yuan, Ke, Robertson, David L. — Nature Communications, 2026-02-19
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• Evolutionary-guided advanced deep-learning architecture powers mammalian GPCRome agonist predictions.
  Aayushi Mittal, Mudit Gupta, Sanjay Kumar Mohanty, A. Gaur, Saveena Solanki, Syed Yasser Ali, A. Raza, Pranjal Sharma, Suvendu Kumar, Vishakha Gautam, Shiva Satija, Nikhil Katyayan, Kunal Tiwari, Anmol Kumar Sharma, D. Kaur, Debarka Sengupta, Shashi Kumar Gupta, Gaurav Ahuja — Cell reports, 2026-02-18
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• De novo covalent drug generation with enhanced drug-likeness and safety
  Zhang, Wenbo, Liu, Tianxiao, Dong, Xiaoying, Sun, Saisai, Yao, Xiaojun, Li, Pengyong, Gao, Lin — Communications Biology, 2026-02-17
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• Enabling multi-target drug discovery through latent evolutionary optimization and synthesis-aware prioritization (EVOSYNTH)
  Nguyen, Viet Thanh Duy, Pham, Phuc, Hy, Truong-Son — Communications Chemistry, 2026-02-16
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• Exploring chemistry and catalysis by biasing skewed distributions via deep learning
  Zhang, Zhikun, Piccini, GiovanniMaria — Nature Communications, 2026-02-21
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• Synthesis of covalent organic frameworks for photocatalytic hydrogen peroxide production guided by large language models
  Shu, Chang, Wang, Ledu, Yang, Xiaoju, Xie, Wenao, Xie, Peixuan, Wang, Xiao, Yang, Xuan, Rao, Jingyi, Wang, Kewei, Chen, Linjiang, Tan, Bien, Wang, Xiaoyan — Nature Communications, 2026-02-21
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• Machine learning guided discovery of water stable metal-organic frameworks for photocatalytic hydrogen production.
  Xiao Niu, Zhiming Zhang, Xiaoyu Wu, Yan Liu, Yong Cui, Jianwen Jiang — Chemical science, 2026-02-18
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• DeepFit: Physically and Chemically Informed XAS-Structure Fitting Made Simple.
  Kirill Kulaev, B. Protsenko, Weiren Cheng, Qinghua Liu, D. Gorbunov, Mikhail Lifar, Valery G. Vlasenko, Anatolii Burlov, S. Guda, Alexander Guda, M. Soldatov, Alexander Soldatov — The journal of physical chemistry letters, 2026-02-19
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• Hybrid Computational Strategy for Predicting Complex Ligand-Metal Architectures.
  Galymzhan Moldagulov, Kisung Lee, Sanzhar Nurgaliyev, Assanali Salem, A. Kuznietsov, B. Grzybowski — Angewandte Chemie, 2026-02-21
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• PROTAC-Splitter: a machine learning framework for automated identification of PROTAC substructures
  Ribes, Stefano, Zhang, Ranxuan, Cropsal, Télio, Källberg, Anders, Tyrchan, Christian, Nittinger, Eva, Mercado, Rocío — Journal of Cheminformatics, 2026-02-20
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• Polypharmacology Browser PPB3: A Web-Based Deep Learning Tool for Target Prediction Using ChEMBL Data.
  M. Darsaraee, S. Javor, J. Reymond — Journal of chemical information and modeling, 2026-02-21
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• RGTFormer: Predicting mutation-associated multi-drug resistance in Mycobacterium tuberculosis using a categorical gated transformer and relational graph convolutional network.
  R. Joshi, Hitesh Reddy Dereddy, Sandip Mukhopadhyay, Radim Burget, M. Dutta — Computational biology and chemistry, 2026-02-18
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• TaLiRAGen: target-aware ligand generation via retrieval-augmented large language models
  Nan, Xiaofei, You, Xing, Liu, Xuezhen, Liu, Hongde, Ji, Chengxiang, Du, Yongsheng, Song, Jinshuai — Molecular Diversity, 2026-02-22
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🧬 Section 2: Preprints (arXiv + bioRxiv)

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

• 🧬 Accurate Macromolecular Complex Modeling for Cryo-EM with CryoZeta
  Zhang, Z.; Li, S.; Farheen, F.; Kagaya, Y.; Liu, B.; Ibtehaz, N.; Terashi, G.; Nakamura, T.; Zhu, H.; Khan, K.; Zhang, Y.; Kihara, D. — bioRxiv, 2026-02-16
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• 🧬 ProteomeLM: A proteome-scale language model enables accurate and rapid prediction of protein-protein interactions and gene essentiality across taxa
  Malbranke, C.; Zalaffi, G. P.; Bitbol, A.-F. — bioRxiv, 2026-02-17
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• 🧬 Deep models of protein evolution in time generate realistic evolutionary trajectories and functional proteins
  Koehl, A.; Prillo, S.; Liu, M.; Xiong, J.; Weng, L.; Savage, D. F.; Song, Y. S. — bioRxiv, 2026-02-20
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• 🧬 Fine-tuning protein language models on human spatial constraint improves variant effect prediction by reducing wild-type sequence bias
  Bajracharya, G.; Capra, J. A. — bioRxiv, 2026-02-19
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• 🧬 Pan-cell-type prediction of splicing patterns from sequence and splicing factor expression
  Vetsigian, K.; Lancaster, J.; Ieremie, I.; Radens, C. M.; Smyth, P.; Young, S. — bioRxiv, 2026-02-19
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• 🧬 Generative AI Guided Design of High-Affinity T cell Receptors
  Min, M. R.; Li, T.; Onoguchi, K.; Mori, D.; Demachi-Okamura, A.; Warrell, J.; Machart, P.; Moesch, A.; Meiser, A.; Pait, I. G.; Muraoka, D.; Matsushita, H.; Paiardi, G.; Ferraz, M.; Bendjama, K. — bioRxiv, 2026-02-18
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• 🧬 ProtFlow: Flow Matching-based Protein Sequence Design with Comprehensive Protein Semantic Distribution Learning and High-quality Generation
  Kong, Z.; Zhu, Y.; Xu, Y.; Yin, M.; Hou, T.; Wu, J.; Xu, H.; Hsieh, C.-Y. — bioRxiv, 2026-02-17
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• 📄 BindCLIP: A Unified Contrastive-Generative Representation Learning Framework for Virtual Screening
  Anjie Qiao, Zhen Wang, Yaliang Li, Jiahua Rao, Yuedong Yang — arXiv, 2026-02-16
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• 🧬 SpecLig: Energy-Guided Hierarchical Model for Target-Specific 3D Ligand Design
  Zhang, P.; Han, R.; Kong, X.; Chen, T.; Ma, J. — bioRxiv, 2026-02-19
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• 📄 MolCrystalFlow: Molecular Crystal Structure Prediction via Flow Matching
  Cheng Zeng, Harry W. Sullivan, Thomas Egg, Maya M. Martirossyan, Philipp Höllmer, Jirui Jin, Richard G. Hennig, Adrian Roitberg, Stefano Martiniani, Ellad B. Tadmor, Mingjie Liu — arXiv, 2026-02-17
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• 📄 Enhanced Diffusion Sampling: Efficient Rare Event Sampling and Free Energy Calculation with Diffusion Models
  Yu Xie, Ludwig Winkler, Lixin Sun, Sarah Lewis, Adam E. Foster, José Jiménez Luna, Tim Hempel, Michael Gastegger, Yaoyi Chen, Iryna Zaporozhets, Cecilia Clementi, Christopher M. Bishop, Frank Noé — arXiv, 2026-02-18
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• 🧬 Towards inferring atomic scale conformation landscape of biomolecules from cryo-electron tomography data
  Feyzi, F. S.; Jonic, S. — bioRxiv, 2026-02-17
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• 🧬 BOND-PEP: topology-conditioned bipartite alignment for evidence-grounded peptide binder generation
  Ding, W. — bioRxiv, 2026-02-18
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• 🧬 Distilling Protein Language Models with Complementary Regularizers
  Wijaya, E. — bioRxiv, 2026-02-18
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• 🧬 MolDeBERTa: Foundational Model for Physicochemical and Structural-Informed Molecular Representation Learning
  de Oliveira, G. B.; Saeed, F. — bioRxiv, 2026-02-17
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• 🧬 Multi-modal tissue-aware graph neural network for in silico genetic discovery
  Aggarwal, A.; Sokolova, K.; Troyanskaya, O. G. — bioRxiv, 2026-02-18
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• 🧬 OncoBERT: Context-Aware Modeling of Somatic Mutations for Precision Oncology
  Patkar, S.; Auslander, N.; Harmon, S.; Choyke, P.; Turkbey, B. — bioRxiv, 2026-02-19
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• 🧬 Fast structural search for classification of gut bacterial mucin O-glycan degrading enzymes
  Erden, M.; Schult, T.; Yanagi, K.; Sahoo, J. K.; Kaplan, D. L.; Cowen, L. J.; Lee, K. — bioRxiv, 2026-02-18
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• 🧬 Harnessing DNA Foundation Models for Cross-Species Transcription Factor Binding Site Prediction in Plant Genomes
  Haghani, M.; Dhulipalla, K. V.; Li, S. — bioRxiv, 2026-02-19
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• 🧬 Bacterial protein function prediction via multimodal deep learning
  Muzio, G.; Adamer, M.; Fernandez, L.; Miklautz, L.; Borgwardt, K.; Avican, K. — bioRxiv, 2026-02-22
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