arXiv AI

Leveraging Biokinetic Knowledge Priors for Data-Scarce Bioprocess Modeling

arXiv:2607. 20539v1 Announce Type: cross Abstract: While deep learning has accelerated drug discovery, its impact on biomanufacturing has been considerably more limited.

arXiv AI

Jun 9

Knowledge-Inclusive Adaptive Physics-Informed Neural Network for Microbial Interaction Modelling

arXiv:2606. 07686v1 Announce Type: cross Abstract: Physics-Informed Neural Network (PINN) is a way of including knowledge in the form of equations in Machine Learning methods.

By Ravisha Rupasinghe, Rajith Vidanaarachchi, Asela Hevapathige, Sachith Seneviratne, Sen-Lin Tang, Saman Halgamuge

benchmarks

arXiv AI

Aug 11

Biologically Informed Representation Learning for Robust Cross-Center Generalization of MALDI-TOF Mass Spectrometry

arXiv:2608. 08182v1 Announce Type: cross Abstract: Machine learning models for MALDI-TOF mass spectrometry have shown considerable promise for clinical microbiology tasks such as microbial identification and antimicrobial resistance prediction.

By Alejandro L. Garc\'ia-Navarro, Carlos Sevilla-Salcedo, Bel\'en Rodr\'iguez-S\'anchez, Vanessa G\'omez-Verdejo

benchmarks safety

arXiv Machine Learning

Jun 25

OmegAMP: Targeted AMP Discovery via Biologically Informed Generation

arXiv:2504. 17247v3 Announce Type: replace Abstract: Deep learning-based antimicrobial peptide (AMP) discovery faces critical challenges such as limited controllability, lack of representations that efficiently model antimicrobial properties, and low experimental hit rates.

By Diogo Soares, Leon Hetzel, Paulina Szymczak, Marcelo Der Torossian Torres, Johanna Sommer, Cesar de la Fuente-Nunez, Fabian Theis, Stephan G\"unnemann, Ewa Szczurek

diffusion benchmarks

arXiv AI

Aug 14

Novel Knowledge-Guided Generative Methods for Synthetic Transcriptomic Data

arXiv:2608. 13256v1 Announce Type: cross Abstract: As biomedical research increasingly relies on data-intensive tools, the quality and utility of datasets are critical.

By Francesca Pia Panaccione, Sofia Mongardi, Marco Masseroli, Pietro Pinoli

benchmarks safety

arXiv Machine Learning

Jun 30

Friend or Foe

arXiv:2509. 00123v2 Announce Type: replace-cross Abstract: A fundamental challenge in microbial ecology is determining whether bacteria compete or cooperate in different environmental conditions.

By Oleksandr Cherednichenko, Josephine Solowiej-Wedderburn, Laura M. Carroll, Eric Libby

benchmarks

arXiv AI

Jun 30

MARS: A neurosymbolic approach for interpretable drug discovery

arXiv:2410. 05289v4 Announce Type: replace Abstract: Background: Neurosymbolic (NeSy) artificial intelligence describes the combination of logic or rule-based techniques with neural networks.

By Lauren Nicole DeLong, Yojana Gadiya, Paola Galdi, Jacques D. Fleuriot, Daniel Domingo-Fern\'andez

benchmarks safety

arXiv AI

Aug 3

Can Large Language Models Derive New Knowledge? A Dynamic Benchmark for Biological Knowledge Discovery

arXiv:2603. 03322v2 Announce Type: replace-cross Abstract: Recent advancements in Large Language Model (LLM) agents have demonstrated remarkable potential in automatic knowledge discovery.

By Chaoqun Yang, Xinyu Lin, Shulin Li, Wenjie Wang, Ruihan Guo, Fuli Feng, Tat-Seng Chua

llms agents benchmarks

arXiv AI

Sep 2

MMAI Gym for Science: Training Liquid Foundation Models for Drug Discovery

arXiv:2603.03517v2 Announce Type: replace-cross Abstract: General-purpose large language models (LLMs) that rely on in-context learning do not reliably deliver the scientific understanding and perfor...

By Maksim Kuznetsov, Zulfat Miftahutdinov, Rim Shayakhmetov, Mikolaj Mizera, Roman Schutski, Bogdan Zagribelnyy, Ivan Ilin, Nikita Bondarev, Thomas MacDougall, Mathieu Reymond, Mihir Bafna, Kaeli Kaymak-Loveless, Eugene Babin, Maxim Malkov, Mathias Lechner, Ramin Hasani, Alexander Amini, Vladimir Aladinskiy, Alex Aliper, Alex Zhavoronkov

llms benchmarks

arXiv AI

Aug 25

RetroDFM-R: Reasoning-Driven Retrosynthesis Prediction with Large Language Models via Reinforcement Learning

arXiv:2507.17448v2 Announce Type: replace-cross Abstract: Retrosynthetic planning is a cornerstone of organic synthesis and drug discovery. Yet existing AI methods often rely on pattern matching rath...

By Situo Zhang, Hanqi Li, Lu Chen, Zihan Zhao, Xuanze Lin, Zichen Zhu, Danyu Luo, Bo Chen, Xin Chen, Kai Yu

llms reinforcement-learning benchmarks safety

arXiv AI

Jun 17

Agentic Discovery of Non-Canonical Antimicrobial Peptides with AMPGAN v3

arXiv:2606. 17127v1 Announce Type: cross Abstract: Antimicrobial resistance causes to over a million deaths annually.

By Jay Jung, Xiaohan Zhang, Shenghan Song, Mahmoud Sayedahmed, Chijian Xiang, Yunong Xu, Ahmed AbdelKhalek, Severin T. Schneebeli, Matthew J. Wargo, Jianing Li, Safwan Wshah

agents diffusion safety

arXiv Machine Learning

Jun 9

Active Flow Expansion for Out-of-Distribution Discovery: from Theory to Molecules

arXiv:2606. 08802v1 Announce Type: new Abstract: Standard flow and diffusion pre-training matches the distribution of available data (e.

By Riccardo De Santi, Bruce Lee, Cristian Perez Jensen, Kimon Protopapas, Sophia Tang, Cheng-Hao Liu, Pranam Chatterjee, Yisong Yue, Andreas Krause

diffusion

arXiv Machine Learning

Sep 2

Freeze, Diffuse, Decode: Geometry-Aware Adaptation of Pretrained Transformer Embeddings for Antimicrobial Peptide Design

arXiv:2511.23120v2 Announce Type: replace Abstract: Pretrained transformers provide rich, general-purpose embeddings, which are transferred to downstream tasks. However, current transfer strategies:...

By Pankhil Gawade, Adam Izdebski, Myriam Lizotte, Kevin R. Moon, Jake S. Rhodes, Guy Wolf, Ewa Szczurek

llms rag diffusion fine-tuning

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