Weekly BioML Digest [March 16, 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.

📚 Peer-Reviewed Journals (Top 20)

491 matched filters -> 20 selected after LLM relevance + novelty ranking.

• De novo design of functional nucleic acids of aptamers
  Zhang, Zhiming, Jiang, Meng, He, Axin, Zhu, Youyuan, Wang, Ercheng, Wan, Liqi, Qiu, Jiezhong, Guo, Pei, Chen, Guangyong, Han, Da — Nature Computational Science, 2026-03-11
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• Design of miniprotein inhibitors targeting complement C9 to block membrane attack complex assembly
  Li, Min, Wang, Ningning, Fu, Xiaoyan, Wei, Gege, Zhang, Ze, Yu, Yanghan, Xue, Tianshui, Zhao, Yifei, Pan, Jinheng, Wang, Dongfeng, Liu, Meifang, Li, Yong, Tang, Jinbao, Cao, Longxing, Jian, Zhaocheng, Liang, Shujuan, Yu, Bowen — Nature Communications, 2026-03-12
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• AF2BIND: predicting small-molecule binding sites using the pair representation of AlphaFold2
  Artem Gazizov, Anna Lian, Casper A. Goverde, Jody Mou, Sergey Ovchinnikov, Nicholas F. Polizzi — Nature Methods, 2026-03-11
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• Learning data-efficient coarse-grained molecular dynamics from forces and noise
  Durumeric, Aleksander E. P., Chen, Yaoyi, Pasos-Trejo, Aldo S., Noé, Frank, Clementi, Cecilia — Nature Communications, 2026-03-15
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• EPInformer: scalable and integrative prediction of gene expression from promoter-enhancer sequences with multimodal epigenomic profiles
  Lin, Jiecong, Li, Zhijian, Zhao, Yajie, Luo, Ruibang, Pinello, Luca — Nature Communications, 2026-03-14
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• Label-free mass and size characterization of few-kDa biomolecules by hierarchical vision transformer augmented nanofluidic scattering microscopy
  K. Moberg, Henrik, Yeroshenko, Bohdan, Fritzsche, Joachim, Albinsson, David, Spackova, Barbora, Midtvedt, Daniel, Volpe, Giovanni, Langhammer, Christoph — Nature Communications, 2026-03-13
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• AI‐Based Prediction of PROTAC‐ and Molecular Glue‐Mediated Ternary Complexes: A Comparative Evaluation of AlphaFold 3 and Boltz‐2
  Lino Riepenhausen, Anne-Christin Sarnow, D. Robaa, W. Sippl — Archiv Der Pharmazie, 2026-03-14
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• A paired sequence language model for protein-protein interaction modeling
  Liu, Jun, Chen, Hungyu, Zhang, Yang — Nature Communications, 2026-03-10
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• Retentive Network promotes efficient RNA language modeling of long sequences
  Shen, Yi, Cao, Guangshuo, Hu, Yueming, Zhang, Shilong, Wu, Jianghong, Chen, Dijun, Chen, Ming — Communications Biology, 2026-03-11
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• A meta learning and task adaptive approach for drug target affinity prediction
  Wan, Mengxuan, Zhao, Yanpeng, Zhang, Yixin, Xu, Huiyan, Yi, Duoyun, Zan, Peng, He, Song, Bo, Xiaochen — Nature Communications, 2026-03-10
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• KANPM-DTA: improving drug–target affinity prediction with Kolmogorov–Arnold networks and pretrained models
  M. Rakib, Muhammad Habibulla Alamin, Jiamu Li, Sheikh Sohan Mamun, Kaleb Amsalu Gobena, Shengbing Ren — Briefings in Bioinformatics, 2026-03-12
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• Fast Generation of Simulation-Quality Structural Ensembles of Mixed-Chirality Cyclic Peptides via Diffusion Models.
  Nomindari Bayaraa, Maxim Secor, Marc L. Descoteaux, Yu-Shan Lin — Journal of chemical theory and computation, 2026-03-12
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• AttenDRP: A Cross-Attention Network for Cancer Drug Response Prediction by Integrating Multi-Omics Data
  Qiu Xiao, Li Wang, Yan Zhang, Ying Zuo, Jiancheng Zhong, Jiawei Luo — Current Bioinformatics, 2026-03-12
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• MSCMF-DTB: a multi-scale cross-modal fusion framework for drug–target binding prediction
  Huang, Juan, Pan, Yuxue, Chen, Qu — Scientific Reports, 2026-03-12
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• DTBAffinity: A Multi-Modal Feature Engineering and Gradient-Boosting Framework for Drug–Target Binding Affinity on Davis and KIBA Benchmarks
  Meshari Alazmi — Computers, 2026-03-10
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• Machine learning assisted single-molecule sensing towards standard-free quantification of per- and polyfluoroalkyl carboxylic acids
  Zuo, Jiaqi, Li, Hong-Shuang, Tang, Wen, Zhao, Xian, Cheng, Meng-Yuan, Yang, Zekai, Tian, Siyu, Li, Pufeng, Xie, Xueying, Luo, Dan, Qiu, Kaipei — Nature Communications, 2026-03-13
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• Accelerated discovery of cell migration regulators using label-free deep learning–based automated tracking
  Tiffany Chu, Yeongseo Lim, Yufei Sun, Fan Wu, Carolina Castillo, Eban A. Hanna, Denis Wirtz, Pei-Hsun Wu — Science Advances, 2026-03-11
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• Machine learning-based proteogenomic data modeling identifies circulating plasma biomarkers for early detection of lung cancer
  Johnson, Marcela A., Nieves-Rodriguez, Shirley, Hou, Liping, Huang, Bevan Emma, Saadatpour, Assieh, Doostparast Torshizi, Abolfazl — Communications Medicine, 2026-03-13
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• Artificial intelligence assisted multi-model pathological diagnosis of breast cancer based on multispectral autofluorescence images
  Sun, Jiahong, Ye, Jianqiao, Chen, Siyi, Yang, Zitong, Xu, Ge, Xue, Yuanbo, Ou, Zi, Chen, Xingye, Wang, Jiandong — npj Breast Cancer, 2026-03-12
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• Minimal correlation but complementary diagnostic utility for plasma cell-free RNA and proteins
  Bliss, Andrew, Loy, Conor J., Kim, Jihoon, Shimizu, Chisato, Lenz, Joan S., Belcher, Emma, Tremoulet, Adriana H., Burns, Jane C., De Vlaminck, Iwijn — Communications Medicine, 2026-03-13
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🧬 Preprints (arXiv + bioRxiv)

145 matched filters -> 20 selected after LLM relevance + novelty ranking.

• 🧬 Programming Biomolecular Interactions with All-Atom Generative Model
  Kong, X.; Chen, J.; Zhang, Z.; Li, G.; Zhu, Q.; Wei, L.; Li, M.; Shi, Y.; Dai, W.; Zhang, Z.; Tan, W.; Jiao, R.; Wang, X.; Zheng, J.; Yu, Z.; Wu, Q.; Guo, Z.; Zhang, L.; Li, W.; Huang, Q.; Zhu, T.; Wang, X.; Huang, W.; She, Y.; Zhang, J.; Liu, Y.; Liu, K.; Ma, J. — bioRxiv, 2026-03-15
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• 🧬 Learning the All-Atom Equilibrium Distribution of Biomolecular Interactions at Scale
  Wang, Y.; Xu, Y.; Li, W.; Yu, H.; Tan, W.; Li, S.; Huang, Q.; Chen, N.; Wu, X.; Wu, Q.; Liu, K. — bioRxiv, 2026-03-13
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• 🧬 Fleming: An AI Agent for Antibiotic Discovery in Mycobacterium Tuberculosis
  Wei, Z.; Ektefaie, Y.; Zhou, A.; Negatu, D.; Aldridge, B. B.; Dick, T. B.; Skarlinski, M.; White, A.; Rodriques, S. G.; Hosseiniporgham, S.; Parai, M.; Flores, A.; Inna, K. V.; Zitnik, M.; Sacchettini, J.; Farhat, M. R. — bioRxiv, 2026-03-12
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• 🧬 What comes after de novo? Automated lead optimization of proteins with CRADLE-1
  Bixby, E.; Brunner, G.; Danciu, D.; Dela Rosa, R.; Deutschmann, N.; Ferragu, C.; Geiger, F.; Holberg, C.; Kidger, P.; Lindoulsi, A.; Lutz, N.; McColgan, T.; Milius, S.; Shah, J.; Vandeloo, M.; Vidas, P.; Ziegler, J. D.; van Rossum, H.; van der Vorm, D.; Baldi, N.; IJSpeert, C.; Monza, E.; Schriek, A. — bioRxiv, 2026-03-12
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• 🧬 A DNA foundation model predicts osteoporosis risk genes without proximity bias
  Regep, C.; Kapourani, C.-A.; Sofyali, E.; Dobrowolska, A.; Loukas, G.; Anighoro, A.; Canale, E.; Gross, T.; Licciardello, M.; Gupta, R.; Maciuca, S.; Desai, T.; Del Vecchio, A.; Field, C.; Gemayel, K.; Javer, A.; Zhang, Z.; Tsujikawa, R.; Inoue, F.; Hessel, E.; Taylor-King, J.; Whittaker, J.; Roblin, D.; McIntyre, R.; Edwards, L. — bioRxiv, 2026-03-12
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• 🧬 A Universal, AI-based Design Framework for Efficient Manufacturing of mRNA Therapeutics
  Liao, K.-C.; Maccari, G.; Ciano, G.; Huber, R.; von der Haar, T.; Tham, C.-Y.; Ting Xun Ong, N.; Florez de Sessions, P.; Yih Saw, T.; Wei Lim, T.; Martin, C.; Dickman, M.; Kis, Z.; Makatsoris, H.; van Asbeck, A.; Wan, Y.; Medini, D. — bioRxiv, 2026-03-10
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• 🧬 Bacterial proteome foundation model enhances functional prediction from enzymes to ecological interactions
  Sethi, P.; Pereira, L. S.; Zhou, J. — bioRxiv, 2026-03-10
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• 🧬 Fast, accurate construction of multiple sequence alignments from protein language embeddings
  Hoang, M.; Armour-Garb, I.; Singh, M. — bioRxiv, 2026-03-10
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• 🧬 Protein Diffusion Models as Statistical Potentials
  Roney, J.; Ou, C.; Ovchinnikov, S. — bioRxiv, 2026-03-13
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• 🧬 Generative AI-based design of hybrid transcriptional activator proteins with new DNA-binding specificity
  Okuda, S. L.; Minami, A.; Aiko, M.; Uetsuka, K.; Miyazaki, K.; Ohtake, K.; Kiga, D. — bioRxiv, 2026-03-13
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• 🧬 SpliceSelectNet: A Hierarchical Transformer-Based Deep Learning Model for Splice Site Prediction
  Miyachi, Y.; Nakai, K. — bioRxiv, 2026-03-12
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• 🧬 Integrated proteomic screening reveals design principles of CRBN molecular glue degraders
  Shashikadze, B.; Scheller, I.; Winkler, D.; Zanon, P. R. A.; Bednarz, A.; Bartoschek, D.; Machata, S.; Graef, T.; Ohmayer, U.; Schwalb, B.; Daub, H.; Steger, M. — bioRxiv, 2026-03-10
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• 🧬 Reaction-Conditioned Enzyme Discovery with Multimodal Deep Learning
  Tan, P. — bioRxiv, 2026-03-10
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• 📄 How to make the most of your masked language model for protein engineering
  Calvin McCarter, Nick Bhattacharya, Sebastian W. Ober, Hunter Elliott — arXiv, 2026-03-11
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• 📄 EvoFlows: Evolutionary Edit-Based Flow-Matching for Protein Engineering
  Nicolas Deutschmann, Constance Ferragu, Jonathan D. Ziegler, Shayan Aziznejad, Eli Bixby — arXiv, 2026-03-12
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• 🧬 PatchDNA: A Flexible and Biologically-Informed Alternative to Tokenization for DNA
  Del Vecchio, A.; Kapourani, C.-A.; Athar, A. M.; Dobrowolska, A.; Anighoro, A.; Tenmann, B.; Edwards, L.; Regep, C. — bioRxiv, 2026-03-12
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• 🧬 Phylogeny-informed transfer learning with protein language models for epitope prediction
  Leite, L. P.; de Campos, T. E.; Lobo, F. P.; Campelo, F. — bioRxiv, 2026-03-10
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• 📄 Co-Diffusion: An Affinity-Aware Two-Stage Latent Diffusion Framework for Generalizable Drug-Target Affinity Prediction
  Yining Qian, Pengjie Wang, Yixiao Li, An-Yang Lu, Cheng Tan, Shuang Li, Lijun Liu — arXiv, 2026-03-11
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• 📄 Protein Counterfactuals via Diffusion-Guided Latent Optimization
  Weronika Kłos, Sidney Bender, Lukas Kades — arXiv, 2026-03-11
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• 📄 Chem4DLLM: 4D Multimodal LLMs for Chemical Dynamics Understanding
  Xinyu Li, Zhen Zhang, Qi Chen, Anton van den Hengel, Lina Yao, Javen Qinfeng Shi — arXiv, 2026-03-12
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