pwc-archive/papers-with-abstracts huggingface dataset:
Graphon Mean Field Games with a Representative Player: Analysis and Learning Algorithm
Authors: Fuzhong Zhou, Chenyu Zhang, Xu Chen, Xuan Di
Date: 2024-05-08 00:00:00

We propose a discrete time graphon game formulation on continuous state and action spaces using a representative player to study stochastic games with heterogeneous interaction among agents. This formulation admits both philosophical and mathematical advantages, compared to a widely adopted formulation using a continuum of players. We prove the existence and uniqueness of the graphon equilibrium with mild assumptions, and show that this equilibrium can be used to construct an approximate solution for finite player game on networks, which is challenging to analyze and solve due to curse of dimensionality. An online oracle-free learning algorithm is developed to solve the equilibrium numerically, and sample complexity analysis is provided for its convergence.
How forest insect outbreaks depend on forest size and tree distribution: an individual-based model results
Authors: Janusz Uchmański
Date: 2024-05-14 00:00:00

In this work, an individual-based model of forest insect outbreaks is presented. The results obtained show that the outbreak is an emerging feature of the system. It is a common product of the characteristics of insects, the environment in which the insects live, and the way insects behave in it. The outbreak dynamics is an effect of scale. In a sufficiently large forest regardless of the density of trees and their spatial distribution, provided that the range of insect dispersion is large enough, it develops in the form of an outbreak. In very small forests, the dynamics becomes more chaotic. It loses the outbreak character and, especially in the forest with random tree distribution, there is a possibility that the insect population goes extinct. The local dynamics of the number of insects on one tree in a forest, where the dynamics of all insects has the character of outbreak, is characterized by a rapid increase in number and then a rapid decrease until the extinction of the local population. It is the result of the influx of immigrants from neighboring trees. The type of tree distribution in the forest becomes visible when the density of trees becomes low and/or the range of insect dispersion is small. When trees are uniformly distributed and the range of insect dispersion is small, the system persists as a set of more or less isolated local populations. In the forest with randomly distributed trees, the insect population becomes more susceptible to extinction when the tree density and/or range of insect dispersion are small.
On Joint Marginal Expected Shortfall and Associated Contribution Risk Measures
Authors: Tong Pu, Yifei Zhang, Yiying Zhang
Date: 2024-05-13 00:00:00

Systemic risk is the risk that a company- or industry-level risk could trigger a huge collapse of another or even the whole institution. Various systemic risk measures have been proposed in the literature to quantify the domino and (relative) spillover effects induced by systemic risks such as the well-known CoVaR, CoES, MES and CoD risk measures, and associated contribution measures. This paper proposes another new type of systemic risk measure, called the joint marginal expected shortfall (JMES), to measure whether the MES of one entity's risk-taking adds to another one or the overall risk conditioned on the event that the entity is already in some specified distress level. We further introduce two useful systemic risk contribution measures based on the difference function or relative ratio function of the JMES and the conventional ES, respectively. Some basic properties of these proposed measures are studied such as monotonicity, comonotonic additivity, non-identifiability and non-elicitability. For both risk measures and two different vectors of bivariate risks, we establish sufficient conditions imposed on copula structure, stress levels, and stochastic orders to compare these new measures. We further provide some numerical examples to illustrate our main findings. A real application in analyzing the risk contagion among several stock market indices is implemented to show the performances of our proposed measures compared with other commonly used measures including CoVaR, CoES, MES, and their associated contribution measures.
Coherent Risk Measure on $L^0$: NA Condition, Pricing and Dual Representation
Authors: Emmanuel Lepinette, Duc Thinh Vu
Date: 2024-05-10 00:00:00

The NA condition is one of the pillars supporting the classical theory of financial mathematics. We revisit this condition for financial market models where a dynamic risk-measure defined on $L^0$ is fixed to characterize the family of acceptable wealths that play the role of non negative financial positions. We provide in this setting a new version of the fundamental theorem of asset pricing and we deduce a dual characterization of the super-hedging prices (called risk-hedging prices) of a European option. Moreover, we show that the set of all risk-hedging prices is closed under NA. At last, we provide a dual representation of the risk-measure on $L^0$ under some conditions.
No-arbitrage conditions and pricing from discrete-time to continuous-time strategies
Authors: Dorsaf Cherif, Emmanuel Lepinette
Date: 2024-05-10 00:00:00

In this paper, a general framework is developed for continuous-time financial market models defined from simple strategies through conditional topologies that avoid stochastic calculus and do not necessitate semimartingale models. We then compare the usual no-arbitrage conditions of the literature, e.g. the usual no-arbitrage conditions NFL, NFLVR and NUPBR and the recent AIP condition. With appropriate pseudo-distance topologies, we show that they hold in continuous time if and only if they hold in discrete time. Moreover, the super-hedging prices in continuous time coincide with the discrete-time super-hedging prices, even without any no-arbitrage condition.
Despite Absolute Information Advantages, All Investors Incur Welfare Loss
Authors: Zongxia Liang, Qi Ye
Date: 2024-05-07 00:00:00
Tasks: All

This paper delves into financial markets that incorporate a novel form of heterogeneity among investors, specifically in terms of their beliefs regarding the reliability of signals in the business cycle economy model, which may be biased. Unlike most papers in this field, we not only analyze the equilibrium but also examine welfare using objective measures while investors aim to maximize their utility based on subjective measures. Furthermore, we introduce passive investors and use their utility as a benchmark, thereby revealing the phenomenon of double loss sometimes. In the analysis, we examine two effects: the distortion effect on total welfare and the advantage effect of information and highlight their key factors of influence, with a particular emphasis on the proportion of investors. We also demonstrate that manipulating investors' estimation towards the economy can be a way to improve utility and identify an inner connection between welfare and survival.
Evaluation of In vitro anti-inflammatory activity and Insilico pharmacokinetics and molecular docking study of Horsfieldia iryaghedhi
Authors: Rajapaksha HKK, Fernando MN, Nelumdeniya NRM, Bandara AWMKK, Silva ARN
Date: 2024-05-13 00:00:00
Tasks: Molecular Docking

Phytochemicals are still a valuable source to develop clinically important drugs in treating chronic and acute diseases. Inflammation is a response to an injurious stimulus of the body and novel therapeutic agents are needed to alleviate the condition with minimum side effects. Matured and fully expanded fresh leaves and barks of H. iryaghedhi were collected, and the extractions were obtained cold maceration using 99.9% methanol and distilled water as solvents. A concentration series was then developed, and the anti-inflammatory activity was evaluated against Diclofenac sodium as the positive control, using the heat-induced egg albumin denaturation method. Further, selected phytochemicals were tested against COX-2 enzyme (PDB ID: 5IKR) using site-specific molecular docking with autodock vina and the binding energies and pharmacokinetic and toxicity parameters were evaluated. Results: The methanol and aqueous extracts have shown a moderate to strong concentration-dependent anti-inflammatory activity with reference to standard Diclofenac sodium and Methanol bark extract exhibited potent anti-inflammatory activity compared to other extracts . Further, Methanol and aqueous extracts showed a statistically significant correlation between concentration and percentage inhibition. The molecular docking results suggest that the phytochemicals available on the plant have possible COX-2 inhibitory activity and the compounds selected (Methyl 2,4- dihydroxy-6-methylbenzoate and N, N-Dimethyl-5-methoxy tryptamine) even got favourable toxicity and pharmacokinetic parameters confirming their drugability.
SurfPro: Functional Protein Design Based on Continuous Surface
Authors: Zhenqiao Song, Tinglin Huang, Lei LI, Wengong Jin
Date: 2024-05-07 00:00:00
Tasks: Decoder, Protein Design

How can we design proteins with desired functions? We are motivated by a chemical intuition that both geometric structure and biochemical properties are critical to a protein's function. In this paper, we propose SurfPro, a new method to generate functional proteins given a desired surface and its associated biochemical properties. SurfPro comprises a hierarchical encoder that progressively models the geometric shape and biochemical features of a protein surface, and an autoregressive decoder to produce an amino acid sequence. We evaluate SurfPro on a standard inverse folding benchmark CATH 4.2 and two functional protein design tasks: protein binder design and enzyme design. Our SurfPro consistently surpasses previous state-of-the-art inverse folding methods, achieving a recovery rate of 57.78% on CATH 4.2 and higher success rates in terms of protein-protein binding and enzyme-substrate interaction scores.
Protein Representation Learning by Capturing Protein Sequence-Structure-Function Relationship
Authors: Eunji Ko, Seul Lee, Minseon Kim, DongKi Kim
Date: 2024-04-29 00:00:00
Tasks: Representation Learning

The goal of protein representation learning is to extract knowledge from protein databases that can be applied to various protein-related downstream tasks. Although protein sequence, structure, and function are the three key modalities for a comprehensive understanding of proteins, existing methods for protein representation learning have utilized only one or two of these modalities due to the difficulty of capturing the asymmetric interrelationships between them. To account for this asymmetry, we introduce our novel asymmetric multi-modal masked autoencoder (AMMA). AMMA adopts (1) a unified multi-modal encoder to integrate all three modalities into a unified representation space and (2) asymmetric decoders to ensure that sequence latent features reflect structural and functional information. The experiments demonstrate that the proposed AMMA is highly effective in learning protein representations that exhibit well-aligned inter-modal relationships, which in turn makes it effective for various downstream protein-related tasks.
Language Interaction Network for Clinical Trial Approval Estimation
Authors: Chufan Gao, Tianfan Fu, Jimeng Sun
Date: 2024-04-26 00:00:00

Clinical trial outcome prediction seeks to estimate the likelihood that a clinical trial will successfully reach its intended endpoint. This process predominantly involves the development of machine learning models that utilize a variety of data sources such as descriptions of the clinical trials, characteristics of the drug molecules, and specific disease conditions being targeted. Accurate predictions of trial outcomes are crucial for optimizing trial planning and prioritizing investments in a drug portfolio. While previous research has largely concentrated on small-molecule drugs, there is a growing need to focus on biologics-a rapidly expanding category of therapeutic agents that often lack the well-defined molecular properties associated with traditional drugs. Additionally, applying conventional methods like graph neural networks to biologics data proves challenging due to their complex nature. To address these challenges, we introduce the Language Interaction Network (LINT), a novel approach that predicts trial outcomes using only the free-text descriptions of the trials. We have rigorously tested the effectiveness of LINT across three phases of clinical trials, where it achieved ROC-AUC scores of 0.770, 0.740, and 0.748 for phases I, II, and III, respectively, specifically concerning trials involving biologic interventions.
ControlMol: Adding Substructure Control To Molecule Diffusion Models
Authors: Qi Zhengyang, Liu Zijing, Zhang Jiying, Cao He, Li Yu
Date: 2024-04-22 00:00:00
Tasks: 3D Molecule Generation, Drug Design, valid

Due to the vast design space of molecules, generating molecules conditioned on a specific sub-structure relevant to a particular function or therapeutic target is a crucial task in computer-aided drug design. Existing works mainly focus on specific tasks, such as linker design or scaffold hopping, each task requires training a model from scratch, and many well-pretrained De Novo molecule generation model parameters are not effectively utilized. To this end, we propose a two-stage training approach, consisting of condition learning and condition optimization. In the condition learning stage, we adopt the idea of ControlNet and design some meaningful adjustments to make the unconditional generative model learn sub-structure conditioned generation. In the condition optimization stage, by using human preference learning, we further enhance the stability and robustness of sub-structure control. In our experiments, only trained on randomly partitioned sub-structure data, the proposed method outperforms previous techniques by generating more valid and diverse molecules. Our method is easy to implement and can be quickly applied to various pre-trained molecule generation models.
ProteinEngine: Empower LLM with Domain Knowledge for Protein Engineering
Authors: Yiqing Shen, Outongyi Lv, Houying Zhu, Yu Guang Wang
Date: 2024-04-21 00:00:00
Tasks: In-Context Learning, Protein Design, Protein Function Prediction

Large language models (LLMs) have garnered considerable attention for their proficiency in tackling intricate tasks, particularly leveraging their capacities for zero-shot and in-context learning. However, their utility has been predominantly restricted to general tasks due to an absence of domain-specific knowledge. This constraint becomes particularly pertinent in the realm of protein engineering, where specialized expertise is required for tasks such as protein function prediction, protein evolution analysis, and protein design, with a level of specialization that existing LLMs cannot furnish. In response to this challenge, we introduce \textsc{ProteinEngine}, a human-centered platform aimed at amplifying the capabilities of LLMs in protein engineering by seamlessly integrating a comprehensive range of relevant tools, packages, and software via API calls. Uniquely, \textsc{ProteinEngine} assigns three distinct roles to LLMs, facilitating efficient task delegation, specialized task resolution, and effective communication of results. This design fosters high extensibility and promotes the smooth incorporation of new algorithms, models, and features for future development. Extensive user studies, involving participants from both the AI and protein engineering communities across academia and industry, consistently validate the superiority of \textsc{ProteinEngine} in augmenting the reliability and precision of deep learning in protein engineering tasks. Consequently, our findings highlight the potential of \textsc{ProteinEngine} to bride the disconnected tools for future research in the protein engineering domain.
RNA Secondary Structure Prediction Using Transformer-Based Deep Learning Models
Authors: Yanlin Zhou, Tong Zhan, Yichao Wu, Bo Song, Chenxi Shi
Date: 2024-04-14 00:00:00
Tasks: Deep Learning, Prediction

The Human Genome Project has led to an exponential increase in data related to the sequence, structure, and function of biomolecules. Bioinformatics is an interdisciplinary research field that primarily uses computational methods to analyze large amounts of biological macromolecule data. Its goal is to discover hidden biological patterns and related information. Furthermore, analysing additional relevant information can enhance the study of biological operating mechanisms. This paper discusses the fundamental concepts of RNA, RNA secondary structure, and its prediction.Subsequently, the application of machine learning technologies in predicting the structure of biological macromolecules is explored. This chapter describes the relevant knowledge of algorithms and computational complexity and presents a RNA tertiary structure prediction algorithm based on ResNet. To address the issue of the current scoring function's unsuitability for long RNA, a scoring model based on ResNet is proposed, and a structure prediction algorithm is designed. The chapter concludes by presenting some open and interesting challenges in the field of RNA tertiary structure prediction.
PROflow: An iterative refinement model for PROTAC-induced structure prediction
Authors: Bo Qiang, Wenxian Shi, Yuxuan Song, Menghua Wu
Date: 2024-04-10 00:00:00

Proteolysis targeting chimeras (PROTACs) are small molecules that trigger the breakdown of traditionally ``undruggable'' proteins by binding simultaneously to their targets and degradation-associated proteins. A key challenge in their rational design is understanding their structural basis of activity. Due to the lack of crystal structures (18 in the PDB), existing PROTAC docking methods have been forced to simplify the problem into a distance-constrained protein-protein docking task. To address the data issue, we develop a novel pseudo-data generation scheme that requires only binary protein-protein complexes. This new dataset enables PROflow, an iterative refinement model for PROTAC-induced structure prediction that models the full PROTAC flexibility during constrained protein-protein docking. PROflow outperforms the state-of-the-art across docking metrics and runtime. Its inference speed enables the large-scale screening of PROTAC designs, and computed properties of predicted structures achieve statistically significant correlations with published degradation activities.
A unified cross-attention model for predicting antigen binding specificity to both HLA and TCR molecules
Authors: Chenpeng Yu, Xing Fang, Hui Liu
Date: 2024-04-08 00:00:00
Tasks: Specificity

The immune checkpoint inhibitors have demonstrated promising clinical efficacy across various tumor types, yet the percentage of patients who benefit from them remains low. The bindings between tumor antigens and HLA-I/TCR molecules determine the antigen presentation and T-cell activation, thereby playing an important role in the immunotherapy response. In this paper, we propose UnifyImmun, a unified cross-attention transformer model designed to simultaneously predict the bindings of peptides to both receptors, providing more comprehensive evaluation of antigen immunogenicity. We devise a two-phase strategy using virtual adversarial training that enables these two tasks to reinforce each other mutually, by compelling the encoders to extract more expressive features. Our method demonstrates superior performance in predicting both pHLA and pTCR binding on multiple independent and external test sets. Notably, on a large-scale COVID-19 pTCR binding test set without any seen peptide in training set, our method outperforms the current state-of-the-art methods by more than 10\%. The predicted binding scores significantly correlate with the immunotherapy response and clinical outcomes on two clinical cohorts. Furthermore, the cross-attention scores and integrated gradients reveal the amino-acid sites critical for peptide binding to receptors. In essence, our approach marks a significant step toward comprehensive evaluation of antigen immunogenicity.
Using GANs for De Novo Protein Design Targeting Microglial IL-3R$α$ to Inhibit Alzheimer's Progression
Authors: Arnav Swaroop
Date: 2024-04-05 00:00:00
Tasks: Protein Design

IL-3 is a hemopoietic growth factor that usually targets blood cell precursors; IL-3R is a cytokine receptor that binds to IL-3. However, IL-3 takes on a different role in the context of glial cells in the nervous system, where studies show that the protein IL-3 protects against Alzheimer's disease by activating microglia at their IL-3R receptors, causing the microglia to clear out the tangles caused by the build-up of misfolded Tau proteins. In this study, we seek to ascertain what role the secondary structure of IL-3 plays in its binding with the receptor. The motivation behind this study is to learn more about the mechanism and identify possible drugs that might be able to activate it, in hopes of inhibiting the spread of Alzheimer's Disease. From a preliminary analysis of complexes containing IL-3 and IL-3R, we hypothesized that the binding is largely due to the interactions of three alpha helix structures stretching towards the active site on the receptor. The original Il-3 protein serves as the control in this experiment; the other proteins being tested are generated through several types of computational de novo protein design, where machine learning allows for the production of entirely novel structures. The efficacy of the generated proteins is assessed through docking simulations with the IL-3R receptor, and the binding poses are also qualitatively examined to gain insight into the function of the binding. From the docking data and poses, the most successful proteins were those with similar secondary structure to IL-3.
ProLLM: Protein Chain-of-Thoughts Enhanced LLM for Protein-Protein Interaction Prediction
Authors: Mingyu Jin, Haochen Xue, Zhenting Wang, Boming Kang, Ruosong Ye, Kaixiong Zhou, Mengnan Du, Yongfeng Zhang
Date: 2024-03-30 00:00:00

The prediction of protein-protein interactions (PPIs) is crucial for understanding biological functions and diseases. Previous machine learning approaches to PPI prediction mainly focus on direct physical interactions, ignoring the broader context of nonphysical connections through intermediate proteins, thus limiting their effectiveness. The emergence of Large Language Models (LLMs) provides a new opportunity for addressing this complex biological challenge. By transforming structured data into natural language prompts, we can map the relationships between proteins into texts. This approach allows LLMs to identify indirect connections between proteins, tracing the path from upstream to downstream. Therefore, we propose a novel framework ProLLM that employs an LLM tailored for PPI for the first time. Specifically, we propose Protein Chain of Thought (ProCoT), which replicates the biological mechanism of signaling pathways as natural language prompts. ProCoT considers a signaling pathway as a protein reasoning process, which starts from upstream proteins and passes through several intermediate proteins to transmit biological signals to downstream proteins. Thus, we can use ProCoT to predict the interaction between upstream proteins and downstream proteins. The training of ProLLM employs the ProCoT format, which enhances the model's understanding of complex biological problems. In addition to ProCoT, this paper also contributes to the exploration of embedding replacement of protein sites in natural language prompts, and instruction fine-tuning in protein knowledge datasets. We demonstrate the efficacy of ProLLM through rigorous validation against benchmark datasets, showing significant improvement over existing methods in terms of prediction accuracy and generalizability. The code is available at: https://github.com/MingyuJ666/ProLLM.