pwc-archive/papers-with-abstracts huggingface dataset:
ProtInvTree: Deliberate Protein Inverse Folding with Reward-guided Tree Search
Authors: Mengdi Liu, Xiaoxue Cheng, Zhangyang Gao, Hong Chang, Cheng Tan, Shiguang Shan, Xilin Chen
Date: 2025-06-01 00:00:00
Tasks: Denoising

Designing protein sequences that fold into a target 3D structure, known as protein inverse folding, is a fundamental challenge in protein engineering. While recent deep learning methods have achieved impressive performance by recovering native sequences, they often overlook the one-to-many nature of the problem: multiple diverse sequences can fold into the same structure. This motivates the need for a generative model capable of designing diverse sequences while preserving structural consistency. To address this trade-off, we introduce ProtInvTree, the first reward-guided tree-search framework for protein inverse folding. ProtInvTree reformulates sequence generation as a deliberate, step-wise decision-making process, enabling the exploration of multiple design paths and exploitation of promising candidates through self-evaluation, lookahead, and backtracking. We propose a two-stage focus-and-grounding action mechanism that decouples position selection and residue generation. To efficiently evaluate intermediate states, we introduce a jumpy denoising strategy that avoids full rollouts. Built upon pretrained protein language models, ProtInvTree supports flexible test-time scaling by expanding the search depth and breadth without retraining. Empirically, ProtInvTree outperforms state-of-the-art baselines across multiple benchmarks, generating structurally consistent yet diverse sequences, including those far from the native ground truth.
Phenotypic Profile-Informed Generation of Drug-Like Molecules via Dual-Channel Variational Autoencoders
Authors: Hui Liu, Shiye Tian, Xuejun Liu
Date: 2025-06-01 00:00:00

The de novo generation of drug-like molecules capable of inducing desirable phenotypic changes is receiving increasing attention. However, previous methods predominantly rely on expression profiles to guide molecule generation, but overlook the perturbative effect of the molecules on cellular contexts. To overcome this limitation, we propose SmilesGEN, a novel generative model based on variational autoencoder (VAE) architecture to generate molecules with potential therapeutic effects. SmilesGEN integrates a pre-trained drug VAE (SmilesNet) with an expression profile VAE (ProfileNet), jointly modeling the interplay between drug perturbations and transcriptional responses in a common latent space. Specifically, ProfileNet is imposed to reconstruct pre-treatment expression profiles when eliminating drug-induced perturbations in the latent space, while SmilesNet is informed by desired expression profiles to generate drug-like molecules. Our empirical experiments demonstrate that SmilesGEN outperforms current state-of-the-art models in generating molecules with higher degree of validity, uniqueness, novelty, as well as higher Tanimoto similarity to known ligands targeting the relevant proteins. Moreover, we evaluate SmilesGEN for scaffold-based molecule optimization and generation of therapeutic agents, and confirmed its superior performance in generating molecules with higher similarity to approved drugs. SmilesGEN establishes a robust framework that leverages gene signatures to generate drug-like molecules that hold promising potential to induce desirable cellular phenotypic changes.
ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models
Authors: Zhuo Chen, Yizhen Zheng, Huan Yee Koh, Hongxin Xiang, Linjiang Chen, Wenjie Du, Yang Wang
Date: 2025-06-01 00:00:00
Tasks: Benchmarking, Relational Reasoning

Molecular Relational Learning (MRL) aims to understand interactions between molecular pairs, playing a critical role in advancing biochemical research. With the recent development of large language models (LLMs), a growing number of studies have explored the integration of MRL with LLMs and achieved promising results. However, the increasing availability of diverse LLMs and molecular structure encoders has significantly expanded the model space, presenting major challenges for benchmarking. Currently, there is no LLM framework that supports both flexible molecular input formats and dynamic architectural switching. To address these challenges, reduce redundant coding, and ensure fair model comparison, we propose ModuLM, a framework designed to support flexible LLM-based model construction and diverse molecular representations. ModuLM provides a rich suite of modular components, including 8 types of 2D molecular graph encoders, 11 types of 3D molecular conformation encoders, 7 types of interaction layers, and 7 mainstream LLM backbones. Owing to its highly flexible model assembly mechanism, ModuLM enables the dynamic construction of over 50,000 distinct model configurations. In addition, we provide comprehensive results to demonstrate the effectiveness of ModuLM in supporting LLM-based MRL tasks.
Improving Protein Sequence Design through Designability Preference Optimization
Authors: Fanglei Xue, Andrew Kubaney, Zhichun Guo, Joseph K. Min, Ge Liu, Yi Yang, David Baker
Date: 2025-05-30 00:00:00
Tasks: Protein Design

Protein sequence design methods have demonstrated strong performance in sequence generation for de novo protein design. However, as the training objective was sequence recovery, it does not guarantee designability--the likelihood that a designed sequence folds into the desired structure. To bridge this gap, we redefine the training objective by steering sequence generation toward high designability. To do this, we integrate Direct Preference Optimization (DPO), using AlphaFold pLDDT scores as the preference signal, which significantly improves the in silico design success rate. To further refine sequence generation at a finer, residue-level granularity, we introduce Residue-level Designability Preference Optimization (ResiDPO), which applies residue-level structural rewards and decouples optimization across residues. This enables direct improvement in designability while preserving regions that already perform well. Using a curated dataset with residue-level annotations, we fine-tune LigandMPNN with ResiDPO to obtain EnhancedMPNN, which achieves a nearly 3-fold increase in in silico design success rate (from 6.56% to 17.57%) on a challenging enzyme design benchmark.
State-aware protein-ligand complex prediction using AlphaFold3 with purified sequences
Authors: Enming Xing, Junjie Zhang, Shen Wang, Xiaolin Cheng
Date: 2025-05-30 00:00:00
Tasks: Multiple Sequence Alignment

Deep learning-based prediction of protein-ligand complexes has advanced significantly with the development of architectures such as AlphaFold3, Boltz-1, Chai-1, Protenix, and NeuralPlexer. Multiple sequence alignment (MSA) has been a key input, providing coevolutionary information critical for structural inference. However, recent benchmarks reveal a major limitation: these models often memorize ligand poses from training data and perform poorly on novel chemotypes or dynamic binding events involving substantial conformational changes in binding pockets. To overcome this, we introduced a state-aware protein-ligand prediction strategy leveraging purified sequence subsets generated by AF-ClaSeq - a method previously developed by our group. AF-ClaSeq isolates coevolutionary signals and selects sequences that preferentially encode distinct structural states as predicted by AlphaFold2. By applying MSA-derived conformational restraints, we observed significant improvements in predicting ligand poses. In cases where AlphaFold3 previously failed-producing incorrect ligand placements and associated protein conformations-we were able to correct the predictions by using sequence subsets corresponding to the relevant functional state, such as the inactive form of an enzyme bound to a negative allosteric modulator. We believe this approach represents a powerful and generalizable strategy for improving protein-ligand complex predictions, with potential applications across a broad range of molecular modeling tasks.
Refining Platelet Purification Methods: Enhancing Proteomics for Clinical Applications
Authors: Vibecke Markhus, Katarina Fritz-Wallace, Olav Mjaavatten, Einar K. Kristoffersen, Dorota Goplen, Frode Selheim
Date: 2025-05-30 00:00:00
Tasks: Sensitivity, TAG

Background: Platelet proteomics offers valuable insights for clinical research, yet isolating high-purity platelets remains a challenge. Current methods often lead to contamination or platelet loss, compromising data quality and reproducibility. Objectives: This study aimed to optimize a platelet isolation technique that yields high-purity samples with minimal loss and to identify the most effective mass spectrometry-based proteomic method for analyzing platelet proteins with optimal coverage and sensitivity. Methods: We refined an isolation protocol by adjusting centrifugation time to reduce blood volume requirements while preserving platelet yield and purity. Using this optimized method, we evaluated three proteomic approaches: Label-free Quantification with Data-Independent Acquisition (LFQ-DIA), Label-free Quantification with Data-Dependent Acquisition (LFQ-DDA), and Tandem Mass Tag labeling with DDA (TMT-DDA). Results: LFQ-DIA demonstrated superior protein coverage and sensitivity compared to LFQ-DDA and TMT-DDA. The refined isolation protocol effectively minimized contamination and platelet loss. Additionally, age-related differences in platelet protein composition were observed, highlighting the importance of using age-matched controls in biomarker discovery studies. Conclusions: The optimized platelet isolation protocol provides a cost-effective and reliable method for preparing high-purity samples for proteomics. LFQ-DIA is the most suitable approach for comprehensive platelet protein analysis. Age-related variation in platelet proteomes underscores the need for demographic matching in clinical proteomic research.
Template-Guided 3D Molecular Pose Generation via Flow Matching and Differentiable Optimization
Authors: Noémie Bergues, Arthur Carré, Paul Join-Lambert, Brice Hoffmann, Arnaud Blondel, Hamza Tajmouati
Date: 2025-05-22 00:00:00
Tasks: Drug Design, Pose Prediction

Predicting the 3D conformation of small molecules within protein binding sites is a key challenge in drug design. When a crystallized reference ligand (template) is available, it provides geometric priors that can guide 3D pose prediction. We present a two-stage method for ligand conformation generation guided by such templates. In the first stage, we introduce a molecular alignment approach based on flow-matching to generate 3D coordinates for the ligand, using the template structure as a reference. In the second stage, a differentiable pose optimization procedure refines this conformation based on shape and pharmacophore similarities, internal energy, and, optionally, the protein binding pocket. We evaluate our approach on a new benchmark of ligand pairs co-crystallized with the same target and show that it outperforms standard docking tools and open-access alignment methods, especially in cases involving low similarity to the template or high ligand flexibility.
GLProtein: Global-and-Local Structure Aware Protein Representation Learning
Authors: Yunqing Liu, Wenqi Fan, XiaoYong Wei, Qing Li
Date: 2025-05-17 00:00:00
Tasks: Representation Learning, Triplet

Proteins are central to biological systems, participating as building blocks across all forms of life. Despite advancements in understanding protein functions through protein sequence analysis, there remains potential for further exploration in integrating protein structural information. We argue that the structural information of proteins is not only limited to their 3D information but also encompasses information from amino acid molecules (local information) to protein-protein structure similarity (global information). To address this, we propose \textbf{GLProtein}, the first framework in protein pre-training that incorporates both global structural similarity and local amino acid details to enhance prediction accuracy and functional insights. GLProtein innovatively combines protein-masked modelling with triplet structure similarity scoring, protein 3D distance encoding and substructure-based amino acid molecule encoding. Experimental results demonstrate that GLProtein outperforms previous methods in several bioinformatics tasks, including predicting protein-protein interaction, contact prediction, and so on.
MolTextNet: A Two-Million Molecule-Text Dataset for Multimodal Molecular Learning
Authors: Yihan Zhu, Gang Liu, Eric Inae, Meng Jiang
Date: 2025-05-15 00:00:00
Tasks: Drug Discovery, Informativeness, Property Prediction, Text Generation

Small molecules are essential to drug discovery, and graph-language models hold promise for learning molecular properties and functions from text. However, existing molecule-text datasets are limited in scale and informativeness, restricting the training of generalizable multimodal models. We present MolTextNet, a dataset of 2.5 million high-quality molecule-text pairs designed to overcome these limitations. To construct it, we propose a synthetic text generation pipeline that integrates structural features, computed properties, bioactivity data, and synthetic complexity. Using GPT-4o-mini, we create structured descriptions for 2.5 million molecules from ChEMBL35, with text over 10 times longer than prior datasets. MolTextNet supports diverse downstream tasks, including property prediction and structure retrieval. Pretraining CLIP-style models with Graph Neural Networks and ModernBERT on MolTextNet yields improved performance, highlighting its potential for advancing foundational multimodal modeling in molecular science. Our dataset is available at https://huggingface.co/datasets/liuganghuggingface/moltextnet.
A novel sensitivity analysis method for agent-based models stratifies in-silico tumor spheroid simulations
Authors: Edward H. Rohr, John T. Nardini
Date: 2025-05-30 00:00:00
Tasks: parameter estimation, Sensitivity

Agent-based models (ABMs) are widely used in biology to understand how individual actions scale into emergent population behavior. Modelers employ sensitivity analysis (SA) algorithms to quantify input parameters' impact on model outputs, however, it is hard to perform SA for ABMs due to their computational and complex nature. In this work, we develop the Simulate, Summarize, Reduce, Cluster, and Analyze (SSRCA) methodology, a machine-learning based pipeline designed to facilitate SA for ABMs. In particular, SSRCA can achieve the following tasks for ABMS: 1) identify sensitive model parameters, 2) reveal common output model patterns, and 3) determine which input parameter values generate these patterns. We use an example ABM of tumor spheroid growth to showcase how SSRCA provides similar SA results to the popular Sobol' Method while also identifying four common patterns from the ABM and the parameter regions that generate these outputs. This analysis could streamline data-driven tasks, such as parameter estimation, for ABMs by reducing parameter space. While we highlight these results with an ABM on tumor spheroid formation, the SSRCA methodology is broadly applicable to biological ABMs.
Immunometabolism at the Crossroads of Infection: Mechanistic and Systems-Level Perspectives from Host and Pathogen
Authors: Sunayana Malla, Nabia Shahreen, Rajib Saha
Date: 2025-06-02 00:00:00

The emerging field of immunometabolism has underscored the central role of metabolic pathways in orchestrating immune cell function. Far from being passive background processes, metabolic activities actively regulate key immune responses. Fundamental pathways such as glycolysis, the tricarboxylic acid (TCA) cycle, and oxidative phosphorylation critically shape the behavior of immune cells, influencing macrophage polarization, T cell activation, and dendritic cell function. In this review, we synthesize recent advances in immunometabolism, with a focus on the metabolic mechanisms that govern the responses of both innate and adaptive immune cells to bacterial, viral, and fungal pathogens. Drawing on experimental, computational, and integrative methodologies, we highlight how metabolic reprogramming contributes to host defense in response to infection. These findings reveal new opportunities for therapeutic intervention, suggesting that modulation of metabolic pathways could enhance immune function and improve pathogen clearance.
A Probabilistic Framework for Imputing Genetic Distances in Spatiotemporal Pathogen Models
Authors: Haley Stone, Jing Du, Hao Xue, Matthew Scotch, David Heslop, Andreas Züfle, Chandini Raina MacIntyre, Flora Salim
Date: 2025-06-10 00:00:00
Tasks: Imputation

Pathogen genome data offers valuable structure for spatial models, but its utility is limited by incomplete sequencing coverage. We propose a probabilistic framework for inferring genetic distances between unsequenced cases and known sequences within defined transmission chains, using time-aware evolutionary distance modeling. The method estimates pairwise divergence from collection dates and observed genetic distances, enabling biologically plausible imputation grounded in observed divergence patterns, without requiring sequence alignment or known transmission chains. Applied to highly pathogenic avian influenza A/H5 cases in wild birds in the United States, this approach supports scalable, uncertainty-aware augmentation of genomic datasets and enhances the integration of evolutionary information into spatiotemporal modeling workflows.
UniPTMs: The First Unified Multi-type PTM Site Prediction Model via Master-Slave Architecture-Based Multi-Stage Fusion Strategy and Hierarchical Contrastive Loss
Authors: Yiyu Lin, Yan Wang, You Zhou, Xinye Ni, Jiahui Wu, Sen yang
Date: 2025-06-05 00:00:00
Tasks: Domain Generalization, Type prediction

As a core mechanism of epigenetic regulation in eukaryotes, protein post-translational modifications (PTMs) require precise prediction to decipher dynamic life activity networks. To address the limitations of existing deep learning models in cross-modal feature fusion, domain generalization, and architectural optimization, this study proposes UniPTMs: the first unified framework for multi-type PTM prediction. The framework innovatively establishes a "Master-Slave" dual-path collaborative architecture: The master path dynamically integrates high-dimensional representations of protein sequences, structures, and evolutionary information through a Bidirectional Gated Cross-Attention (BGCA) module, while the slave path optimizes feature discrepancies and recalibration between structural and traditional features using a Low-Dimensional Fusion Network (LDFN). Complemented by a Multi-scale Adaptive convolutional Pyramid (MACP) for capturing local feature patterns and a Bidirectional Hierarchical Gated Fusion Network (BHGFN) enabling multi-level feature integration across paths, the framework employs a Hierarchical Dynamic Weighting Fusion (HDWF) mechanism to intelligently aggregate multimodal features. Enhanced by a novel Hierarchical Contrastive loss function for feature consistency optimization, UniPTMs demonstrates significant performance improvements (3.2%-11.4% MCC and 4.2%-14.3% AP increases) over state-of-the-art models across five modification types and transcends the Single-Type Prediction Paradigm. To strike a balance between model complexity and performance, we have also developed a lightweight variant named UniPTMs-mini.
Quantum Ensembling Methods for Healthcare and Life Science
Authors: Kahn Rhrissorrakrai, Kathleen E. Hamilton, Prerana Bangalore Parthsarathy, Aldo Guzman-Saenz, Tyler Alban, Filippo Utro, Laxmi Parida
Date: 2025-06-02 00:00:00
Tasks: Binary Classification

Learning on small data is a challenge frequently encountered in many real-world applications. In this work we study how effective quantum ensemble models are when trained on small data problems in healthcare and life sciences. We constructed multiple types of quantum ensembles for binary classification using up to 26 qubits in simulation and 56 qubits on quantum hardware. Our ensemble designs use minimal trainable parameters but require long-range connections between qubits. We tested these quantum ensembles on synthetic datasets and gene expression data from renal cell carcinoma patients with the task of predicting patient response to immunotherapy. From the performance observed in simulation and initial hardware experiments, we demonstrate how quantum embedding structure affects performance and discuss how to extract informative features and build models that can learn and generalize effectively. We present these exploratory results in order to assist other researchers in the design of effective learning on small data using ensembles. Incorporating quantum computing in these data constrained problems offers hope for a wide range of studies in healthcare and life sciences where biological samples are relatively scarce given the feature space to be explored.
Leveraging Natural Language Processing to Unravel the Mystery of Life: A Review of NLP Approaches in Genomics, Transcriptomics, and Proteomics
Authors: Ella Rannon, David Burstein
Date: 2025-06-02 00:00:00

Natural Language Processing (NLP) has transformed various fields beyond linguistics by applying techniques originally developed for human language to the analysis of biological sequences. This review explores the application of NLP methods to biological sequence data, focusing on genomics, transcriptomics, and proteomics. We examine how various NLP methods, from classic approaches like word2vec to advanced models employing transformers and hyena operators, are being adapted to analyze DNA, RNA, protein sequences, and entire genomes. The review also examines tokenization strategies and model architectures, evaluating their strengths, limitations, and suitability for different biological tasks. We further cover recent advances in NLP applications for biological data, such as structure prediction, gene expression, and evolutionary analysis, highlighting the potential of these methods for extracting meaningful insights from large-scale genomic data. As language models continue to advance, their integration into bioinformatics holds immense promise for advancing our understanding of biological processes in all domains of life.
SPACE: Your Genomic Profile Predictor is a Powerful DNA Foundation Model
Authors: Zhao Yang, Jiwei Zhu, Bing Su
Date: 2025-06-02 00:00:00
Tasks: Mixture-of-Experts, Unsupervised Pre-training

Inspired by the success of unsupervised pre-training paradigms, researchers have applied these approaches to DNA pre-training. However, we argue that these approaches alone yield suboptimal results because pure DNA sequences lack sufficient information, since their functions are regulated by genomic profiles like chromatin accessibility. Here, we demonstrate that supervised training for genomic profile prediction serves as a more effective alternative to pure sequence pre-training. Furthermore, considering the multi-species and multi-profile nature of genomic profile prediction, we introduce our $\textbf{S}$pecies-$\textbf{P}$rofile $\textbf{A}$daptive $\textbf{C}$ollaborative $\textbf{E}$xperts (SPACE) that leverages Mixture of Experts (MoE) to better capture the relationships between DNA sequences across different species and genomic profiles, thereby learning more effective DNA representations. Through extensive experiments across various tasks, our model achieves state-of-the-art performance, establishing that DNA models trained with supervised genomic profiles serve as powerful DNA representation learners. The code is available at https://github.com/ZhuJiwei111/SPACE.