pwc-archive/papers-with-abstracts huggingface dataset:
A stochastic Gordon-Loeb model for optimal cybersecurity investment under clustered attacks
Authors: Giorgia Callegaro, Claudio Fontana, Caroline Hillairet, Beatrice Ongarato
Date: 2025-05-02 00:00:00
Tasks: Clustering, Management

We develop a continuous-time stochastic model for optimal cybersecurity investment under the threat of cyberattacks. The arrival of attacks is modeled using a Hawkes process, capturing the empirically relevant feature of clustering in cyberattacks. Extending the Gordon-Loeb model, each attack may result in a breach, with breach probability depending on the system's vulnerability. We aim at determining the optimal cybersecurity investment to reduce vulnerability. The problem is cast as a two-dimensional Markovian stochastic optimal control problem and solved using dynamic programming methods. Numerical results illustrate how accounting for attack clustering leads to more responsive and effective investment policies, offering significant improvements over static and Poisson-based benchmark strategies. Our findings underscore the value of incorporating realistic threat dynamics into cybersecurity risk management.
Approximation to Deep Q-Network by Stochastic Delay Differential Equations
Authors: Jianya Lu, Yingjun Mo
Date: 2025-05-01 00:00:00

Despite the significant breakthroughs that the Deep Q-Network (DQN) has brought to reinforcement learning, its theoretical analysis remains limited. In this paper, we construct a stochastic differential delay equation (SDDE) based on the DQN algorithm and estimate the Wasserstein-1 distance between them. We provide an upper bound for the distance and prove that the distance between the two converges to zero as the step size approaches zero. This result allows us to understand DQN's two key techniques, the experience replay and the target network, from the perspective of continuous systems. Specifically, the delay term in the equation, corresponding to the target network, contributes to the stability of the system. Our approach leverages a refined Lindeberg principle and an operator comparison to establish these results.
Simulating integrated Volterra square-root processes and Volterra Heston models via Inverse Gaussian
Authors: Eduardo Abi Jaber, Elie Attal
Date: 2025-04-28 00:00:00

We introduce a novel simulation scheme, iVi (integrated Volterra implicit), for integrated Volterra square-root processes and Volterra Heston models based on the Inverse Gaussian distribution. The scheme is designed to handle $L^1$ kernels with singularities by relying solely on integrated kernel quantities, and it preserves the non-decreasing property of the integrated process. We establish weak convergence of the iVi scheme by reformulating it as a stochastic Volterra equation with a measure kernel and proving a stability result for this class of equations. Numerical results demonstrate that convergence is achieved with very few time steps. Remarkably, for the rough fractional kernel, unlike existing schemes, convergence seems to improve as the Hurst index $H$ decreases and approaches $-1/2$.
Score-Based Deterministic Density Sampling
Authors: Vasily Ilin, Peter Sushko, Jingwei Hu
Date: 2025-04-25 00:00:00
Tasks: Diagnostic

We propose a deterministic sampling framework using Score-Based Transport Modeling for sampling an unnormalized target density $\pi$ given only its score $\nabla \log \pi$. Our method approximates the Wasserstein gradient flow on $\mathrm{KL}(f_t\|\pi)$ by learning the time-varying score $\nabla \log f_t$ on the fly using score matching. While having the same marginal distribution as Langevin dynamics, our method produces smooth deterministic trajectories, resulting in monotone noise-free convergence. We prove that our method dissipates relative entropy at the same rate as the exact gradient flow, provided sufficient training. Numerical experiments validate our theoretical findings: our method converges at the optimal rate, has smooth trajectories, and is usually more sample efficient than its stochastic counterpart. Experiments on high dimensional image data show that our method produces high quality generations in as few as 15 steps and exhibits natural exploratory behavior. The memory and runtime scale linearly in the sample size.
Marginal expected shortfall: Systemic risk measurement under dependence uncertainty
Authors: Jinghui Chen, Edward Furman, X. Sheldon Lin
Date: 2025-04-28 00:00:00

Measuring the contribution of a bank or an insurance company to the overall systemic risk of the market is an important issue, especially in the aftermath of the 2007-2009 financial crisis and the financial downturn of 2020. In this paper, we derive the worst-case and best-case bounds for marginal expected shortfall (MES) -- a key measure of systemic risk contribution -- under the assumption of known marginal distributions for individual companies' risks but an unknown dependence structure. We further derive improved bounds for the MES risk measure when partial information on companies' risk exposures -- and hence their dependence -- is available. To capture this partial information, we utilize three commonly used background risk models: the additive, minimum-based, and multiplicative factor models. Finally, we present an alternative set of improved MES bounds based on a linear regression relationship between individual companies' risks and overall market risk, consistent with the assumptions of the Capital Asset Pricing Model in finance and the Weighted Insurance Pricing Model in insurance.
A review of DNA restriction-free overlapping sequence cloning techniques for synthetic biology
Authors: Isabella Frighetto Bomfiglio, Isabelli Seiler de Medeiros Mendes, Diego Bonatto
Date: 2025-05-06 00:00:00
Tasks: TAR

DNA cloning methods are fundamental tools in molecular biology, synthetic biology, and genetic engineering that enable precise DNA manipulation for various scientific and biotechnological applications. This review systematically summarizes the major restriction-free overlapping sequence cloning (RFOSC) techniques currently used in synthetic biology and examines their development, efficiency, practicality, and specific applications. In vitro methods, including Gibson Assembly, Circular Polymerase Extension Cloning (CPEC), Polymerase Incomplete Primer Extension (PIPE), Overlap Extension Cloning (OEC), Flap Endonuclease Cloning (FEN-Cloning), and commercially available techniques such as TOPO and In-Fusion, have been discussed alongside hybrid approaches such as Ligation-Independent Cloning (LIC), Sequence-Independent Cloning (SLIC), and T5 Exonuclease-Dependent Assembly (TEDA). Additionally, in vivo methods leveraging host recombination machinery, including Yeast Homologous Recombination (YHR), In Vivo Assembly (IVA), Transformation-Associated Recombination (TAR), and innovative approaches such as Multiple-Round In Vivo Site-Specific Assembly (MISSA) and Phage Enzyme-Assisted Direct Assembly (PEDA), are critically evaluated. The review highlights that method selection should consider the scale, complexity, cost, and specific needs of individual research projects, noting that no single technique is universally optimal. Future trends suggest the increased integration of enzymatic efficiency, host versatility, and automation, broadening the accessibility and capabilities of DNA assembly technologies.
MAP Format for Representing Chemical Modifications, Annotations, and Mutations in Protein Sequences: An Extension of the FASTA Format
Authors: Akshay Shendre, Naman Kumar Mehta, Anand Singh Rathore, Nishant Kumar, Sumeet Patiyal, Gajendra P. S. Raghava
Date: 2025-05-06 00:00:00

Several formats, including FASTA, PIR, GenBank, EMBL, and GCG, have been developed for representing protein sequences composed of natural amino acids. Among these, FASTA remains the most widely used due to its simplicity and human readability. However, FASTA lacks the capability to represent chemically modified or non-natural residues, as well as structural annotations and mutations in protein variants. To address some of these limitations, the PEFF format was recently introduced as an extension of FASTA. Additionally, formats such as HELM and BILN have been proposed to represent amino acids and their modifications at the atomic level. Despite their advancements, these formats have not achieved widespread adoption within the bioinformatics community due to their complexity. To complement existing formats and overcome current challenges, we propose a new format called MAP (Modification and Annotation in Proteins), which enables comprehensive annotation of protein sequences. MAP introduces meta tags in the header for protein-level annotations and inline tags within the sequence for residue-level modifications. In this format, standard one-letter amino acid codes are augmented with curly-brace tags to denote various modifications, including phosphorylation, acetylation, non-natural residues, cyclization, and other residue-specific features. The header metadata also captures information such as organism, function, and sequence variants. We describe the structure, objectives, and capabilities of the MAP format and demonstrate its application in bioinformatics, particularly in the domain of protein therapeutics. To facilitate community adoption, we are developing a comprehensive suite of MAP-format resources, including a detailed manual, annotated datasets, and conversion tools, available at http://webs.iiitd.edu.in/raghava/maprepo/.
AutoLoop: a novel autoregressive deep learning method for protein loop prediction with high accuracy
Authors: Tianyue Wang, Xujun Zhang, Langcheng Wang, Odin Zhang, Jike Wang, Ercheng Wang, Jialu Wu, Renling Hu, Jingxuan Ge, Shimeng Li, Qun Su, Jiajun Yu, Chang-Yu Hsieh, Tingjun Hou, Yu Kang
Date: 2025-05-06 00:00:00
Tasks: Drug Discovery, Protein Structure Prediction

Protein structure prediction is a critical and longstanding challenge in biology, garnering widespread interest due to its significance in understanding biological processes. A particular area of focus is the prediction of missing loops in proteins, which are vital in determining protein function and activity. To address this challenge, we propose AutoLoop, a novel computational model designed to automatically generate accurate loop backbone conformations that closely resemble their natural structures. AutoLoop employs a bidirectional training approach while merging atom- and residue-level embedding, thus improving robustness and precision. We compared AutoLoop with twelve established methods, including FREAD, NGK, AlphaFold2, and AlphaFold3. AutoLoop consistently outperforms other methods, achieving a median RMSD of 1.12 Angstrom and a 2-Angstrom success rate of 73.23% on the CASP15 dataset, while maintaining strong performance on the HOMSTARD dataset. It demonstrates the best performance across nearly all loop lengths and secondary structural types. Beyond accuracy, AutoLoop is computationally efficient, requiring only 0.10 s per generation. A post-processing module for side-chain packing and energy minimization further improves results slightly, confirming the reliability of the predicted backbone. A case study also highlights AutoLoop's potential for precise predictions based on dominant loop conformations. These advances hold promise for protein engineering and drug discovery.
Scoring-Assisted Generative Exploration for Proteins (SAGE-Prot): A Framework for Multi-Objective Protein Optimization via Iterative Sequence Generation and Evaluation
Authors: Hocheol Lim, Geon-Ho Lee, Kyoung Tai No
Date: 2025-05-02 00:00:00
Tasks: Protein Design

Proteins play essential roles in nature, from catalyzing biochemical reactions to binding specific targets. Advances in protein engineering have the potential to revolutionize biotechnology and healthcare by designing proteins with tailored properties. Machine learning and generative models have transformed protein design by enabling the exploration of vast sequence-function landscapes. Here, we introduce Scoring-Assisted Generative Exploration for Proteins (SAGE-Prot), a framework that iteratively combines autoregressive protein generation with quantitative structure-property relationship models for fine-tuned optimization. By integrating diverse protein descriptors, SAGE-Prot enhances key properties, including binding affinity, thermal stability, enzymatic activity, and solubility. We demonstrate its effectiveness by optimizing GB1 for binding affinity and thermal stability and TEM-1 for enzymatic activity and solubility. Leveraging curriculum learning, SAGE-Prot adapts rapidly to increasingly complex design objectives, building on past successes. Experimental validation demonstrated that SAGE-Prot-generated proteins substantially outperformed their wild-type counterparts, achieving up to a 17-fold increase in beta-lactamase activity, underscoring SAGE-Prot's potential to tackle critical challenges in protein engineering. As generative models continue to evolve, approaches like SAGE-Prot will be indispensable for advancing rational protein design.
Learning Hierarchical Interaction for Accurate Molecular Property Prediction
Authors: Huiyang Hong, Xinkai Wu, Hongyu Sun, Chaoyang Xie, Qi Wang, Yuquan Li
Date: 2025-04-28 00:00:00
Tasks: Drug Discovery, Molecular Property Prediction, Prediction, Property Prediction, Representation Learning

Discovering molecules with desirable molecular properties, including ADMET profiles, is of great importance in drug discovery. Existing approaches typically employ deep learning models, such as Graph Neural Networks (GNNs) and Transformers, to predict these molecular properties by learning from diverse chemical information. However, these models often fail to efficiently capture and utilize the hierarchical nature of molecular structures, and often lack mechanisms for effective interaction among multi-level features. To address these limitations, we propose a Hierarchical Interaction Message Passing Mechanism, which serves as the foundation of our novel model, the Hierarchical Interaction Message Net (HimNet). Our method enables interaction-aware representation learning across atomic, motif, and molecular levels via hierarchical attention-guided message passing. This design allows HimNet to effectively balance global and local information, ensuring rich and task-relevant feature extraction for downstream property prediction tasks, such as Blood-Brain Barrier Permeability (BBBP). We systematically evaluate HimNet on eleven datasets, including eight widely-used MoleculeNet benchmarks and three challenging, high-value datasets for metabolic stability, malaria activity, and liver microsomal clearance, covering a broad range of pharmacologically relevant properties. Extensive experiments demonstrate that HimNet achieves the best or near-best performance in most molecular property prediction tasks. Furthermore, our method exhibits promising hierarchical interpretability, aligning well with chemical intuition on representative molecules. We believe that HimNet offers an accurate and efficient solution for molecular activity and ADMET property prediction, contributing to advanced decision-making in the early stages of drug discovery.
BEER: Biochemical Estimator and Explorer of Residues -- A Comprehensive Software Suite for Protein Sequence Analysis
Authors: Saumyak Mukherjee
Date: 2025-04-29 00:00:00

Protein sequence analysis underpins research in biophysics, computational biology, and bioinformatics. We introduce BEER, a crossplatform graphical interface that accepts FASTA or Protein Data Bank (PDB) files, or manual sequence entry, and instantly computes a suite of physicochemical metrics, such as amino acid composition, Kyte Doolittle hydrophobicity profiles, net charge versus pH curves with automatic isoelectric point determination, solubility predictions, and key indices such as molecular weight, extinction coefficient, GRAVY (grand average of hydropathicity) score, instability index, and aromaticity. BEER's interactive visualizations, including bar and pie charts, hydropathy plots, residue level bead models, and radar diagrams make it easy to explore physicochemical properties of protein chains. A multichain module also enables direct comparison of complex assemblies. Built in Python with BioPython, PyQt5, and matplotlib, BEER delivers complete analyses of sequences up to 10000 residues in under one second.
Pan-genome Analysis of Angiosperm Plastomes using PGR-TK
Authors: Manoj P. Samanta
Date: 2025-04-28 00:00:00

We present a novel approach for taxonomic analysis of chloroplast genomes in angiosperms using the Pan-genome Research Toolkit (PGR-TK). Comparative plots generated by PGR-TK across diverse angiosperm genera reveal a wide range of structural complexity, from straightforward to highly intricate patterns. Notably, the characteristic quadripartite plastome structure, comprising the large single copy (LSC), small single copy (SSC), and inverted repeat (IR) regions, is clearly identifiable in over 75% of the genera analyzed. Our findings also underscore several occurrences of species mis-annotations in public genomic databases, which are readily detected through visual anomalies in the PGR-TK plots. While more complex plot patterns remain difficult to interpret, they likely reflect underlying biological variation or technical inconsistencies in genome assembly. Overall, this approach effectively integrates classical botanical visualization with modern molecular taxonomy, providing a powerful tool for genome-based classification in plant systematics.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction
Authors: Yasir Ghunaim, Andrés Villa, Gergo Ignacz, Gyorgy Szekely, Motasem Alfarra, Bernard Ghanem
Date: 2025-04-28 00:00:00
Tasks: Model Compression, Molecular Property Prediction, Property Prediction

Advancements in machine learning for molecular property prediction have improved accuracy but at the expense of higher computational cost and longer training times. Recently, the Joint Multi-domain Pre-training (JMP) foundation model has demonstrated strong performance across various downstream tasks with reduced training time over previous models. Despite JMP's advantages, fine-tuning it on molecular datasets ranging from small-scale to large-scale requires considerable time and computational resources. In this work, we investigate strategies to enhance efficiency by reducing model size while preserving performance. To better understand the model's efficiency, we analyze the layer contributions of JMP and find that later interaction blocks provide diminishing returns, suggesting an opportunity for model compression. We explore block reduction strategies by pruning the pre-trained model and evaluating its impact on efficiency and accuracy during fine-tuning. Our analysis reveals that removing two interaction blocks results in a minimal performance drop, reducing the model size by 32% while increasing inference throughput by 1.3x. These results suggest that JMP-L is over-parameterized and that a smaller, more efficient variant can achieve comparable performance with lower computational cost. Our study provides insights for developing lighter, faster, and more scalable foundation models for molecular and materials discovery. The code is publicly available at: https://github.com/Yasir-Ghunaim/efficient-jmp.
CAML: Commutative algebra machine learning -- a case study on protein-ligand binding affinity prediction
Authors: Hongsong Feng, Faisal Suwayyid, Mushal Zia, JunJie Wee, Yuta Hozumi, Chunlong Chen, Guo-Wei Wei
Date: 2025-04-25 00:00:00

Recently, Suwayyid and Wei have introduced commutative algebra as an emerging paradigm for machine learning and data science. In this work, we integrate commutative algebra machine learning (CAML) for the prediction of protein-ligand binding affinities. Specifically, we apply persistent Stanley-Reisner theory, a key concept in combinatorial commutative algebra, to the affinity predictions of protein-ligand binding and metalloprotein-ligand binding. We introduce three new algorithms, i.e., element-specific commutative algebra, category-specific commutative algebra, and commutative algebra on bipartite complexes, to address the complexity of data involved in (metallo) protein-ligand complexes. We show that the proposed CAML outperforms other state-of-the-art methods in (metallo) protein-ligand binding affinity predictions.
Targeted AMP generation through controlled diffusion with efficient embeddings
Authors: Diogo Soares, Leon Hetzel, Paulina Szymczak, Fabian Theis, Stephan Günnemann, Ewa Szczurek
Date: 2025-04-24 00:00:00
Tasks: Diversity

Deep learning-based antimicrobial peptide (AMP) discovery faces critical challenges such as low experimental hit rates as well as the need for nuanced controllability and efficient modeling of peptide properties. To address these challenges, we introduce OmegAMP, a framework that leverages a diffusion-based generative model with efficient low-dimensional embeddings, precise controllability mechanisms, and novel classifiers with drastically reduced false positive rates for candidate filtering. OmegAMP enables the targeted generation of AMPs with specific physicochemical properties, activity profiles, and species-specific effectiveness. Moreover, it maximizes sample diversity while ensuring faithfulness to the underlying data distribution during generation. We demonstrate that OmegAMP achieves state-of-the-art performance across all stages of the AMP discovery pipeline, significantly advancing the potential of computational frameworks in combating antimicrobial resistance.
In-Context Learning can distort the relationship between sequence likelihoods and biological fitness
Authors: Pranav Kantroo, Günter P. Wagner, Benjamin B. Machta
Date: 2025-04-23 00:00:00
Tasks: In-Context Learning, Language Modeling, Language Modelling, Masked Language Modeling

Language models have emerged as powerful predictors of the viability of biological sequences. During training these models learn the rules of the grammar obeyed by sequences of amino acids or nucleotides. Once trained, these models can take a sequence as input and produce a likelihood score as an output; a higher likelihood implies adherence to the learned grammar and correlates with experimental fitness measurements. Here we show that in-context learning can distort the relationship between fitness and likelihood scores of sequences. This phenomenon most prominently manifests as anomalously high likelihood scores for sequences that contain repeated motifs. We use protein language models with different architectures trained on the masked language modeling objective for our experiments, and find transformer-based models to be particularly vulnerable to this effect. This behavior is mediated by a look-up operation where the model seeks the identity of the masked position by using the other copy of the repeated motif as a reference. This retrieval behavior can override the model's learned priors. This phenomenon persists for imperfectly repeated sequences, and extends to other kinds of biologically relevant features such as reversed complement motifs in RNA sequences that fold into hairpin structures.