pwc-archive/papers-with-abstracts huggingface dataset:
Collaborative Expert LLMs Guided Multi-Objective Molecular Optimization
Authors: Jiajun Yu, Yizhen Zheng, Huan Yee Koh, Shirui Pan, Tianyue Wang, Haishuai Wang
Date: 2025-03-05 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

Molecular optimization is a crucial yet complex and time-intensive process that often acts as a bottleneck for drug development. Traditional methods rely heavily on trial and error, making multi-objective optimization both time-consuming and resource-intensive. Current AI-based methods have shown limited success in handling multi-objective optimization tasks, hampering their practical utilization. To address this challenge, we present MultiMol, a collaborative large language model (LLM) system designed to guide multi-objective molecular optimization. MultiMol comprises two agents, including a data-driven worker agent and a literature-guided research agent. The data-driven worker agent is a large language model being fine-tuned to learn how to generate optimized molecules considering multiple objectives, while the literature-guided research agent is responsible for searching task-related literature to find useful prior knowledge that facilitates identifying the most promising optimized candidates. In evaluations across six multi-objective optimization tasks, MultiMol significantly outperforms existing methods, achieving a 82.30% success rate, in sharp contrast to the 27.50% success rate of current strongest methods. To further validate its practical impact, we tested MultiMol on two real-world challenges. First, we enhanced the selectivity of Xanthine Amine Congener (XAC), a promiscuous ligand that binds both A1R and A2AR, successfully biasing it towards A1R. Second, we improved the bioavailability of Saquinavir, an HIV-1 protease inhibitor with known bioavailability limitations. Overall, these results indicate that MultiMol represents a highly promising approach for multi-objective molecular optimization, holding great potential to accelerate the drug development process and contribute to the advancement of pharmaceutical research.
Modeling Iodine Deficiency
Authors: Paul Fong, Albert Li, Zoe Meadows, Rati Pillai, Chelsea Proutt, Alex Schneider
Date: 2025-03-04 00:00:00
Tasks: Diagnostic

This paper presents a four-unit, four-component mathematical model of iodine metabolism and its impact on thyroid hormone levels in the body. We focus on the relationships between iodine (I-), triiodothyronine (T3), thyroxine (T4), and thyroid-stimulating hormone (TSH) through the mixer, thyroid, sensor (pituitary gland), and metabolism. Iodine plays a fundamental role in maintaining metabolic homeostasis, as it is essential for the synthesis of T3 and T4, which regulate weight, energy, and other physiological functions. Iodine deficiency, which is one of the most common nutrient deficiencies in the world, can lead to hypothyroidism, a condition characterized by fatigue, weight gain, and cognitive impairments [1]. Our model tracks the movement of iodine through dietary intake, thyroid absorption, hormone synthesis, feedback regulation via TSH, and deiodization in metabolism. By evaluating different forms of the accounting equation governing these processes, we have concluded three key results. 1) Iodide availability directly impacts levels of T3 and T4 production, with both component flow rates declining at day 70 in a mildly diseased state, and day 60 in a severely diseased state. 2) TSH is an early diagnostic indicator of thyroid issues, with TSH levels rising to magnitudes of 25x in just ten days, nearly six times quicker than other biological indicators. 3) There is a 5 day difference in iodine storage depletion between mild and severe iodide deficiency. Understanding these results quantitatively provides insights into thyroid disorders and informs strategies for managing iodine deficiency on both individual and public health levels.
RiboGen: RNA Sequence and Structure Co-Generation with Equivariant MultiFlow
Authors: Dana Rubin, Allan dos Santos Costa, Manvitha Ponnapati, Joseph Jacobson
Date: 2025-03-03 00:00:00

Ribonucleic acid (RNA) plays fundamental roles in biological systems, from carrying genetic information to performing enzymatic function. Understanding and designing RNA can enable novel therapeutic application and biotechnological innovation. To enhance RNA design, in this paper we introduce RiboGen, the first deep learning model to simultaneously generate RNA sequence and all-atom 3D structure. RiboGen leverages the standard Flow Matching with Discrete Flow Matching in a multimodal data representation. RiboGen is based on Euclidean Equivariant neural networks for efficiently processing and learning three-dimensional geometry. Our experiments show that RiboGen can efficiently generate chemically plausible and self-consistent RNA samples, suggesting that co-generation of sequence and structure is a competitive approach for modeling RNA.
Pushing the boundaries of Structure-Based Drug Design through Collaboration with Large Language Models
Authors: Bowen Gao, Yanwen Huang, Yiqiao Liu, Wenxuan Xie, Wei-Ying Ma, Ya-Qin Zhang, Yanyan Lan
Date: 2025-03-03 00:00:00
Tasks: Drug Design, Drug Discovery

Structure-Based Drug Design (SBDD) has revolutionized drug discovery by enabling the rational design of molecules for specific protein targets. Despite significant advancements in improving docking scores, advanced 3D-SBDD generative models still face challenges in producing drug-like candidates that meet medicinal chemistry standards and pharmacokinetic requirements. These limitations arise from their inherent focus on molecular interactions, often neglecting critical aspects of drug-likeness. To address these shortcomings, we introduce the Collaborative Intelligence Drug Design (CIDD) framework, which combines the structural precision of 3D-SBDD models with the chemical reasoning capabilities of large language models (LLMs). CIDD begins by generating supporting molecules with 3D-SBDD models and then refines these molecules through LLM-supported modules to enhance drug-likeness and structural reasonability. When evaluated on the CrossDocked2020 dataset, CIDD achieved a remarkable success ratio of 37.94%, significantly outperforming the previous state-of-the-art benchmark of 15.72%. Although improving molecular interactions and drug-likeness is often seen as a trade-off, CIDD uniquely achieves a balanced improvement in both by leveraging the complementary strengths of different models, offering a robust and innovative pathway for designing therapeutically promising drug candidates.
Applying computational protein design to therapeutic antibody discovery -- current state and perspectives
Authors: Weronika Bielska, Igor Jaszczyszyn, Pawel Dudzic, Bartosz Janusz, Dawid Chomicz, Sonia Wrobel, Victor Greiff, Ryan Feehan, Jared Adolf-Bryfogle, Konrad Krawczyk
Date: 2025-03-02 00:00:00
Tasks: Drug Discovery, Protein Design

Machine learning applications in protein sciences have ushered in a new era for designing molecules in silico. Antibodies, which currently form the largest group of biologics in clinical use, stand to benefit greatly from this shift. Despite the proliferation of these protein design tools, their direct application to antibodies is often limited by the unique structural biology of these molecules. Here, we review the current computational methods for antibody design, highlighting their role in advancing computational drug discovery.
Leveraging Sequence Purification for Accurate Prediction of Multiple Conformational States with AlphaFold2
Authors: Enming Xing, Junjie Zhang, Shen Wang, Xiaolin Cheng
Date: 2025-02-28 00:00:00
Tasks: Drug Design, Protein Structure Prediction

AlphaFold2 (AF2) has transformed protein structure prediction by harnessing co-evolutionary constraints embedded in multiple sequence alignments (MSAs). MSAs not only encode static structural information, but also hold critical details about protein dynamics, which underpin biological functions. However, these subtle co-evolutionary signatures, which dictate conformational state preferences, are often obscured by noise within MSA data and thus remain challenging to decipher. Here, we introduce AF-ClaSeq, a systematic framework that isolates these co-evolutionary signals through sequence purification and iterative enrichment. By extracting sequence subsets that preferentially encode distinct structural states, AF-ClaSeq enables high-confidence predictions of alternative conformations. Our findings reveal that the successful sampling of alternative states depends not on MSA depth but on sequence purity. Intriguingly, purified sequences encoding specific structural states are distributed across phylogenetic clades and superfamilies, rather than confined to specific lineages. Expanding upon AF2's transformative capabilities, AF-ClaSeq provides a powerful approach for uncovering hidden structural plasticity, advancing allosteric protein and drug design, and facilitating dynamics-based protein function annotation.
Multicellular self-organization in Escherichia coli
Authors: Devina Puri, Kyle R. Allison
Date: 2025-03-04 00:00:00

Escherichia coli has long been a trusty companion, maintaining health in our guts and advancing biological knowledge in the laboratory. In light of recent findings, we discuss multicellular self-organization in E. coli and develop general ideas for multicellularity, including the necessity for multicellular dynamics and interpretation by dynamic graphs, applicable to both unicellular and multicellular organisms. In this context, we next discuss the documented behaviors of E. coli self-organization (rosette formation, multicellular extension, and attached dormancy) and two potential behaviors (internal communication and mating). Finally, by comparing the dynamic graphs for different communities, we develop principles relevant to the theory of multicellularity.
Intercellular contact is sufficient to drive Fibroblast to Myofibroblast transitions
Authors: Vasuretha Chandar, Benjamin M. Goykadosh, Harikrishnan Parameswaran
Date: 2025-03-03 00:00:00

Fibroblast cells play a key role in maintaining the extracellular matrix. During wound healing, fibroblasts differentiate into highly contractile myofibroblasts, which secrete extracellular matrix proteins like collagen to facilitate tissue repair. Under normal conditions, myofibroblasts undergo programmed cell death after healing to prevent excessive scar formation. However, in diseases like fibrosis, the myofibroblasts remain active even after the wound is closed, resulting in excessive collagen buildup and a stiff, fibrotic matrix. The reasons for the persistence of myofibroblasts in fibrosis are not well understood. Here we show the existence of a mechanism where direct physical contact between a fibroblast and a myofibroblast is sufficient for fibroblasts to transition into myofibroblasts. We show that fibroblast-myofibroblast transition can occur even in the absence of known biochemical cues such as growth factor activation or mechanical cues from a stiff, fibrotic matrix. Further, we show that contact-based fibroblast-myofibroblast activation can be blocked by G{\alpha}q/11/14 inhibitor FR9003591, which inhibits the formation of myofibroblasts. These findings offer new insights into the persistence of fibrosis despite therapeutic interventions and suggest a potential strategy to target fibroblast-to-myofibroblast transition in fibrosis.
Fungal Genetic Variants in Oceanic Environments
Authors: Sade A. Davenport, Scott H. Harrison
Date: 2025-03-03 00:00:00

Comparing specific types of organisms as they are found across environmental conditions has helped inform how genes and gene products of these organisms relate to phenotypes and adaptation. In this study, we examine metatranscriptomic data as found for oceanic fungi across different oceanic sampling sites. A specific set of three genes was chosen for evaluation based on conserved orthology, known association with core physiological processes in fungi, and level of abundance within oceanic metatranscriptomic data. We report upon a potential association of genetic variance with environmental conditions of iron, salt and phosphate in oceanic waters based on heatmap visualization and PERMANOVA analysis.
An Efficient Algorithm for Determining the Equivalence of Zero-one Reaction Networks
Authors: Yue Jiao, Xiaoxian Tang
Date: 2025-02-16 00:00:00

Zero-one reaction networks play a crucial role in cell signaling. Determining the equivalence of reaction networks is a fundamental computational problem in the field of chemical reaction networks. In this work, we develop an efficient method for determining the equivalence of zero-one networks. The efficiency comes from several criteria for determining the equivalence of the steady-state ideals arising from zero-one networks, which helps for cutting down the expenses on computing Grobner bases. Experiments show that our method can successfully classify over three million networks according to their equivalence in a reasonable time.
ZAPBench: A Benchmark for Whole-Brain Activity Prediction in Zebrafish
Authors: Jan-Matthis Lueckmann, Alexander Immer, Alex Bo-Yuan Chen, Peter H. Li, Mariela D. Petkova, Nirmala A. Iyer, Luuk Willem Hesselink, Aparna Dev, Gudrun Ihrke, Woohyun Park, Alyson Petruncio, Aubrey Weigel, Wyatt Korff, Florian Engert, Jeff W. Lichtman, Misha B. Ahrens, Michał Januszewski, Viren Jain
Date: 2025-03-04 00:00:00
Tasks: Activity Prediction, Multivariate Time Series Forecasting, Spatio-Temporal Forecasting, Time Series Forecasting, Video Forecasting

Data-driven benchmarks have led to significant progress in key scientific modeling domains including weather and structural biology. Here, we introduce the Zebrafish Activity Prediction Benchmark (ZAPBench) to measure progress on the problem of predicting cellular-resolution neural activity throughout an entire vertebrate brain. The benchmark is based on a novel dataset containing 4d light-sheet microscopy recordings of over 70,000 neurons in a larval zebrafish brain, along with motion stabilized and voxel-level cell segmentations of these data that facilitate development of a variety of forecasting methods. Initial results from a selection of time series and volumetric video modeling approaches achieve better performance than naive baseline methods, but also show room for further improvement. The specific brain used in the activity recording is also undergoing synaptic-level anatomical mapping, which will enable future integration of detailed structural information into forecasting methods.
The Neural Basis of Groove Sensations: Implications for Music-Based Interventions and Dance Therapy in Parkinson's Disease
Authors: Chen-Gia Tsai, Chia-Wei Li
Date: 2025-03-04 00:00:00
Tasks: Rhythm

Groove sensations arise from rhythmic structures that evoke an urge to move in response to music. While syncopation has been extensively studied in groove perception, the neural mechanisms underlying low-frequency groove remain underexplored. This fMRI study examines the role of the mirror neuron system and associated brain regions in processing low-frequency groove. Region-of-interest analysis revealed that amplifying drum and bass components in K-pop songs significantly increased activity in the right posterior inferior frontal gyrus, right inferior/superior parietal lobules, left dorsolateral prefrontal cortex, and bilateral posterior middle/inferior temporal gyrus. These findings suggest that low-frequency grooves engage sensorimotor, executive, and rhythm semantics networks, reinforcing their role in action-related processing. Building on these insights, we propose an enhanced rhythmic auditory stimulation paradigm for Parkinson's disease, incorporating amplified low-frequency rhythmic cues to improve gait synchronization.
MindSimulator: Exploring Brain Concept Localization via Synthetic FMRI
Authors: Guangyin Bao, Qi Zhang, Zixuan Gong, Zhuojia Wu, Duoqian Miao
Date: 2025-03-04 00:00:00

Concept-selective regions within the human cerebral cortex exhibit significant activation in response to specific visual stimuli associated with particular concepts. Precisely localizing these regions stands as a crucial long-term goal in neuroscience to grasp essential brain functions and mechanisms. Conventional experiment-driven approaches hinge on manually constructed visual stimulus collections and corresponding brain activity recordings, constraining the support and coverage of concept localization. Additionally, these stimuli often consist of concept objects in unnatural contexts and are potentially biased by subjective preferences, thus prompting concerns about the validity and generalizability of the identified regions. To address these limitations, we propose a data-driven exploration approach. By synthesizing extensive brain activity recordings, we statistically localize various concept-selective regions. Our proposed MindSimulator leverages advanced generative technologies to learn the probability distribution of brain activity conditioned on concept-oriented visual stimuli. This enables the creation of simulated brain recordings that reflect real neural response patterns. Using the synthetic recordings, we successfully localize several well-studied concept-selective regions and validate them against empirical findings, achieving promising prediction accuracy. The feasibility opens avenues for exploring novel concept-selective regions and provides prior hypotheses for future neuroscience research.
Hebbian learning the local structure of language
Authors: P. Myles Eugenio
Date: 2025-03-03 00:00:00
Tasks: Language Modeling, Language Modelling

Learning in the brain is local and unsupervised (Hebbian). We derive the foundations of an effective human language model inspired by these microscopic constraints. It has two parts: (1) a hierarchy of neurons which learns to tokenize words from text (whichiswhatyoudowhenyoureadthis); and (2) additional neurons which bind the learned symanticless patterns of the tokenizer into a symanticful token (an embedding). The model permits continuous parallel learning without forgetting; and is a powerful tokenizer which performs renormalization group. This allows it to exploit redundancy, such that it generates tokens which are always decomposable into a basis set (e.g an alphabet), and can mix features learned from multiple languages. We find that the structure of this model allows it to learn a natural language morphology WITHOUT data. The language data generated by this model predicts the correct distribution of word-forming patterns observed in real languages, and further demonstrates why microscopically human speech is broken up into words. This model provides the basis for understanding the microscopic origins of language and human creativity.
A comprehensive and reliable protocol for manual segmentation of the human claustrum using high-resolution MRI
Authors: Steven Seung-Suk Kang, Joseph Bodenheimer, Kayley Morris, Tracey Butler
Date: 2025-03-03 00:00:00
Tasks: Anatomy, Segmentation

The claustrum is a thin gray matter structure in each brain hemisphere, characterized by exceptionally high connectivity with nearly all brain regions. Despite extensive animal studies on its anatomy and function and growing evidence of claustral deficits in neuropsychiatric disorders, its specific roles in normal and abnormal human brain function remain largely unknown. This is primarily due to its thin and complex morphology, which limits accurate anatomical delineation and neural activity isolation in conventional in vivo neuroimaging. To facilitate future neuroimaging studies, we developed a comprehensive and reliable manual segmentation protocol based on a cellular-resolution brain atlas and high-resolution (0.7^3 mm) MRI data. The protocols involve detailed guidelines to delineate the entire claustrum, including the inferior parts that have not been clearly described in earlier MRI studies. Additionally, we propose a geometric method to parcellate the claustrum into three subregions (the dorsal, ventral, and temporal claustrum) along the superior-to-inferior axis. The mean bilateral claustrum volume in 10 young adults was 3307.5 mm^3, approximately 0.21% of total intracranial volume. Our segmentation protocol demonstrated high inter- and intra-rater reliability (ICC > 0.89, DSC > 0.85), confirming its replicability. This comprehensive and reliable manual segmentation protocol offers a robust foundation for anatomically precise neuroimaging investigations of the human claustrum.
Learning to Chain Operations by Routing Information Through a Global Workspace
Authors: Hugo Chateau-Laurent, Rufin VanRullen
Date: 2025-02-28 00:00:00

We present a model inspired by the Global Workspace Theory that integrates specialized modules to perform a sequential reasoning task. A controller selectively routes information between modules through the workspace using a gating mechanism. This approach allows the model to chain operations by iteratively broadcasting information between specialized domains, mimicking System-2 reasoning. We evaluate the model's performance on a simple addition task, where two addends must be summed. The task can be solved by routing information sequentially through an Input module, an Increment module (multiple times), and finally an Output module. We consider two implementations of this system with increasing complexity. First, using hand-designed modules operating on one-hot digit representations, the controller (a LSTM recurrent network) learns to select the appropriate modules (input, increment, output) in the appropriate sequence. Second, we replace the hand-designed modules with learned representation modules for MNIST images and an increment module trained on the task objectives; here again, the controller learns the appropriate sequential module selection to solve the task. Finally, we show that the Global Workspace model, while having fewer parameters, outperforms LSTMs and Transformers when tested on unseen addition operations (both interpolations and extrapolations of addition operations seen during training). Our results highlight the potential of architectures inspired by the Global Workspace Theory to enhance deep learning's reasoning capabilities.
Neural Manifolds and Cognitive Consistency: A New Approach to Memory Consolidation in Artificial Systems
Authors: Phuong-Nam Nguyen
Date: 2025-02-25 00:00:00

We introduce a novel mathematical framework that unifies neural population dynamics, hippocampal sharp wave-ripple (SpWR) generation, and cognitive consistency constraints inspired by Heider's theory. Our model leverages low-dimensional manifold representations to capture structured neural drift and incorporates a balance energy function to enforce coherent synaptic interactions, effectively simulating the memory consolidation processes observed in biological systems. Simulation results demonstrate that our approach not only reproduces key features of SpWR events but also enhances network interpretability. This work paves the way for scalable neuromorphic architectures that bridge neuroscience and artificial intelligence, offering more robust and adaptive learning mechanisms for future intelligent systems.