jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Qahtan Adnan ALJANABI | Zhijian Huang | Gebremedhin Assefa Girmay | Zheng Wang | Yuanpeng Zhang | Lei Deng
Source:  (article, 2025-12-15)
Field: Materials Science — Materials Chemistry

DSSA: Dual-Stream Synthetic Accessibility Framework for Organic Compounds Synthetic accessibility prediction remains a key bottleneck in AI-driven drug discovery, as a large proportion of computationally generated molecules prove infeasible to synthesize. Existing approaches often struggle to distinguish structurally similar compounds with divergent synthetic profiles, limiting their usefulness in practical design pipelines. We present DSSA (Dual-Stream Synthetic Accessibility), a novel architecture that integrates Graph Attention Networks for molecular topology with a Bidirectional GRU for sequential SMILES representations through transformer-based cross-modal fusion. DSSA effectively captures both local structural complexity and global sequential patterns, enabling robust generalization across diverse molecular classes. Cross-modal attention analysis reveals that the model dynamically adapts to molecular complexity, with graph-dominant attention highlighting stereochemical constraints that sequenceonly models overlook. Ablation studies further confirm that cross-modal fusion is essential for achieving balanced structural and sequential reasoning. Collectively, DSSA bridges the gap between computational molecular generation and real-world synthetic feasibility, offering a reliable foundation for data-driven molecular design. Web tool: http://dssa.denglab.org/; code/data: https://github.com/Q-Aljanabi/DSSA. Limiting ; Bottleneck ; Synthetic data ; Generalization ; Key (lock) ; Graph Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Shi Wu Li | Hongyan Du | Hui Zhang | Xujun Zhang | Yuanyi Ye | Chao Shen | Tingjun Hou | Peichen Pan
Source: Wiley Interdisciplinary Reviews Computational Molecular Science (article, 2025-11-01)
Field: Chemistry — Organic Chemistry

Accelerating Covalent Drug Discovery: Recent Advances in Covalent Docking Tools ABSTRACT Covalent inhibitors have garnered renewed attention in recent years, with their rational design becoming increasingly critical in drug discovery. Among the technologies facilitating the discovery of covalent inhibitors, covalent docking has emerged as a pivotal tool in various stages of drug development including virtual screening, lead optimization, and mechanistic studies. Since its inception as an extension of conventional docking methods in the early 2000s, covalent docking tools have undergone substantial advancements. This review provides a comprehensive overview of covalent docking algorithms, systematically categorizing their approaches according to covalent bond formation, which primarily include tethered docking, biased docking, and dynamic covalent docking approaches. A comparative analysis of current covalent docking tools is provided, alongside a critical discussion of remaining challenges. Special emphasis is placed on the growing impact of artificial intelligence (AI) in shaping novel methodologies and expanding the capabilities of covalent docking. Finally, we discuss prospects for advancing covalent docking methodologies and their applications in drug discovery. This article is categorized under: Software &gt; Molecular Modeling Covalent bond ; Docking (animal) ; Drug discovery ; Virtual screening ; Non-covalent interactions Click Chemistry and Applications ; Synthesis and Characterization of Pyrroles ; Chemistry and Chemical Engineering
Authors: Kazi Fahim Ahmad Nasif | Bobin Deng | Lingtao Chen | Yixin Xie | Shaolei Teng | Li Jiang | Liang Zhao | S M Shafiul Alam | Nobel Dhar | Kun Suo | Dan Lo
Source:  (article, 2025-11-03)
Field: Materials Science — Materials Chemistry

AccelMD: A Self-Adaptive AI-Enabled Framework for Accelerating Molecular Dynamics Simulations Molecular Dynamics (MD) simulation is a fundamental exploring approach for numerous scientific fields, including but not limited to drug discovery, biology, material science, and chemistry. Unfortunately, large-scale MD simulation generally requires long-period processing, even in resource-intensive supercomputers. Parallel computing is the primary methodology to accelerate MD simulation. However, parallel computing has a low theoretical improvable ceiling for MD simulation because the atom statuses of one timestep depend on its previous timestep. To unlock the full potential of MD simulation in drug discovery and biology, we propose an AccelMD framework for reducing processing time and computing costs. AccelMD is an AI-enabled framework that accelerates MD simulation while maintaining high predictive accuracy. In order to extend AccelMD's applicability and flexibility, we also developed an extra preprocessing stage, which allows the framework to adapt to various protein inputs automatically. Compared to conventional MD, the AccelMD framework achieves 47.59 X speedup on average (max: 63.78 X, min: 30.61 X). Regarding the prediction accuracy of protein structures, the average Mean Absolute Error (MAE) and TMScore values of AccelMD are 0.0058 and 0.99980, respectively. Our empirical experiments exihibit the robustness of AccelMD framework for the study of complex, long-timescale molecular interactions. Molecular dynamics ; Speedup ; Robustness (evolution) ; Preprocessor ; Supercomputer ; Identification (biology) Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Wenrui Zhang | Xinggang Wang | Bin Feng | Wenyu Liu
Source: ArXiv.org (preprint, 2025-11-21)
Field: Materials Science — Materials Chemistry

MolSight: Optical Chemical Structure Recognition with SMILES Pretraining, Multi-Granularity Learning and Reinforcement Learning Optical Chemical Structure Recognition (OCSR) plays a pivotal role in modern chemical informatics, enabling the automated conversion of chemical structure images from scientific literature, patents, and educational materials into machine-readable molecular representations. This capability is essential for large-scale chemical data mining, drug discovery pipelines, and Large Language Model (LLM) applications in related domains. However, existing OCSR systems face significant challenges in accurately recognizing stereochemical information due to the subtle visual cues that distinguish stereoisomers, such as wedge and dash bonds, ring conformations, and spatial arrangements. To address these challenges, we propose MolSight, a comprehensive learning framework for OCSR that employs a three-stage training paradigm. In the first stage, we conduct pre-training on large-scale but noisy datasets to endow the model with fundamental perception capabilities for chemical structure images. In the second stage, we perform multi-granularity fine-tuning using datasets with richer supervisory signals, systematically exploring how auxiliary tasks-specifically chemical bond classification and atom localization-contribute to molecular formula recognition. Finally, we employ reinforcement learning for post-training optimization and introduce a novel stereochemical structure dataset. Remarkably, we find that even with MolSight's relatively compact parameter size, the Group Relative Policy Optimization (GRPO) algorithm can further enhance the model's performance on stereomolecular. Through extensive experiments across diverse datasets, our results demonstrate that MolSight achieves state-of-the-art performance in (stereo)chemical optical structure recognition. Reinforcement learning ; Perception ; Pattern recognition (psychology) ; Training set ; Chemical process ; Wedge (geometry) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Molecular spectroscopy and chirality
Source: Chemistry International (article, 2025-07-01)
Field: Chemistry — Physical and Theoretical Chemistry

How Chemistry can make the difference Place Greater Industry-Academia Collaboration:Pharmaceutical companies should actively explore more partnerships with academic researchers to drive innovation in small-molecule drug development.The notion that: "we can do it ourselves" will soon become irrelevant, as universities may scale back medicinal chemistry programs due to a lack of PIs in the fi eld. 4. Improved Grant Review Processes: Funding agencies should ensure that real medicinal chemistry panels are introduced to the grants calls and that they are populated by reviewers that have expertise in medicinal chemistry. Interdisciplinary Training: Researchers in medic-inal chemistry should expand their expertise in computational chemistry, structural biology, and AI-driven drug discovery to remain competitive in securing funding. ConclusionsThe study underscores an urgent need to address the funding crisis in medicinal chemistry.While scientifi c advancements in biologics and novel therapeutic modalities like cell-or gene-therapy are valuable, small-molecule-based research remains crucial for developing cost-effective and accessible drugs.The authors advocate for immediate intervention by funding agencies, government bodies, and industry leaders to reinstate fi nancial support and safeguard the future of medicinal chemistry.Without corrective measures, the decline in funding could have long-term consequences on drug discovery, pharmaceutical innovation, and global healthcare outcomes especially within developed countries. Chemistry ; Industrial chemistry ; Biochemical engineering ; Polymer science ; Environmental chemistry ; Environmental science ; Engineering History and advancements in chemistry
Authors: J. Urbanus | E.M. de Ruiter
Source: TNO Repository (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

How AI is transforming the field of polymer design Artificial Intelligence (AI) is revolutionizing polymer design, enabling data-driven material discovery and accelerating innovation. Polymer Informatics, the fusion of data science and polymer science, leverages AI models to predict structure-property-function relationships, facilitating both forward and inverse design of novel polymers. This article aims to guide the Polymer Informatics community through three key obstacles: limited and fragmented data, modeling polymers’ complexity adequately, and the need to address practical viability beyond technical performance. This article further aims to highlight opportunities for overcoming these hurdles. Recent advances such as federated learning, high throughput laboratories, active learning, and hybrid AI models offer promising solutions. Integrating safety, sustainability, and economic factors is essential for safe and sustainable polymer design Field (mathematics) ; Key (lock) ; Informatics ; Materials informatics ; Applications of artificial intelligence Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Block Copolymer Self-Assembly
Authors: Muhammad B. Awan | Abdul Razzaq | Abdul Shahid
Source: ArXiv.org (preprint, 2025-10-29)
Field: Materials Science — Materials Chemistry

Benchmarking Generative AI Against Bayesian Optimization for Constrained Multi-Objective Inverse Design This paper investigates the performance of Large Language Models (LLMs) as generative optimizers for solving constrained multi-objective regression tasks, specifically within the challenging domain of inverse design (property-to-structure mapping). This problem, critical to materials informatics, demands finding complex, feasible input vectors that lie on the Pareto optimal front. While LLMs have demonstrated universal effectiveness across generative and reasoning tasks, their utility in constrained, continuous, high-dimensional numerical spaces tasks they weren't explicitly architected for remains an open research question. We conducted a rigorous comparative study between established Bayesian Optimization (BO) frameworks and a suite of fine-tuned LLMs and BERT models. For BO, we benchmarked the foundational BoTorch Ax implementation against the state-of-the-art q-Expected Hypervolume Improvement (qEHVI, BoTorchM). The generative approach involved fine-tuning models via Parameter-Efficient Fine-Tuning (PEFT), framing the challenge as a regression problem with a custom output head. Our results show that BoTorch qEHVI achieved perfect convergence (GD=0.0), setting the performance ceiling. Crucially, the best-performing LLM (WizardMath-7B) achieved a Generational Distance (GD) of 1.21, significantly outperforming the traditional BoTorch Ax baseline (GD=15.03). We conclude that specialized BO frameworks remain the performance leader for guaranteed convergence, but fine-tuned LLMs are validated as a promising, computationally fast alternative, contributing essential comparative metrics to the field of AI-driven optimization. The findings have direct industrial applications in optimizing formulation design for resins, polymers, and paints, where multi-objective trade-offs between mechanical, rheological, and chemical properties are critical to innovation and production efficiency. Benchmarking ; Generative grammar ; Suite ; Bayesian probability ; Framing (construction) ; Inverse ; Multi-objective optimization ; Field (mathematics) Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Generative Adversarial Networks and Image Synthesis
Authors: Trail Biomed
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-11-13)
Field: Materials Science — Materials Chemistry

AI co-scientists for drug development_lighter version AI Co-Scientists in Drug Development AI is changing how we do science — and it’s opening new frontiers in drug discovery. We’re often asked what “AI co-scientists” really are, how they differ, and how they can fit into the fast-moving world of pharma R&D. Here’s how we’re turning that vision into reality — bringing AI co-scientists from concept to impact. Drug ; Drug development ; MEDLINE ; Feature (linguistics) ; Key (lock) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Artificial Intelligence in Healthcare and Education
Authors: Pouya M. Ghari | Simone Sciabola | Yefei Wang
Source: ArXiv.org (preprint, 2025-10-31)
Field: Materials Science — Materials Chemistry

Iterative Foundation Model Fine-Tuning on Multiple Rewards Fine-tuning foundation models has emerged as a powerful approach for generating objects with specific desired properties. Reinforcement learning (RL) provides an effective framework for this purpose, enabling models to generate outputs that maximize a given reward function. However, in many applications such as text generation and drug discovery, it can be suboptimal to optimize using a single reward signal, as multiple evaluation criteria are often necessary. This paper proposes a novel reinforcement learning-based method for fine-tuning foundation models using multiple reward signals. By employing an iterative fine-tuning strategy across these rewards, our approach generalizes state-of-the-art RL-based methods. We further provide a theoretical analysis that offers insights into the performance of multi-reward RL fine-tuning. Experimental results across diverse domains including text, biological sequence, and small molecule generation, demonstrate the effectiveness of the proposed algorithm compared to state-of-the-art baselines. Reinforcement learning ; Foundation (evidence) ; Variety (cybernetics) ; Iterative method ; Iterative and incremental development Machine Learning in Materials Science ; Machine Learning and Data Classification ; Topic Modeling
Authors: Chun-Yue Shi | Yan Liang | Yusheng Wang | Xinyi Zhang | Aparna Kushwaha | Abhinav Kumar | Jun Wang | Qin Ouyang | Yong Huang
Source: Materials Today Chemistry (article, 2025-12-25)
Field: Materials Science — Materials Chemistry

MOF and COF-based drug delivery system for Parkinson's disease: Mechanism, future and development of artificial intelligence Drug delivery ; Disease ; Biosafety ; Biomarker ; Drug development ; Transformative learning Covalent Organic Framework Applications ; Parkinson's Disease Mechanisms and Treatments ; Dendrimers and Hyperbranched Polymers
Authors: E. T. Areh | Olurotimi Demokun | Amina Zubair | S. Sanni
Source: Confluence University Journal of Science and Technology (article, 2025-01-01)
Field: Materials Science — Biomaterials

Computational Insights into the Anticancer Potential of Flavonoids isolated from Calotropis procera (Aiton) Artificial intelligence (AI) is revolutionizing sustainable chemistry that enabling the efficient discovery and analysis of bioactive compounds through computational or bioinformatics method. This study investigates flavonoid compounds isolated from Calotropis procera using AI-driven molecular docking to determine its potential as an anticancer agent against EGFR Kinase domain (PDB ID: 3POZ) which inhibition will lead to the creation of new and novel drugs for cancer treatment with enhanced precision and potency. Flavonoids are a type of polyphenol found in fruits, vegetables and drinks. It possesses potential health benefits, particularly in preventing cancer and reducing oxidative stress. Advanced docking simulations were performed using Schrödinger\'s Maestro suite, which included protein and ligand preparation, receptor binding site was localized to the co crystalized native ligand (03P) and docking was performed in standard precision mode. The flavonoid compounds showed significant binding affinity with docking scores ranging from -8.686 to -5.522 kcal/mol. The native ligand (03P) achieved the highest docking score of -12.803 kcal/mol, indicating the strongest binding affinity. Among the isolated flavonoids: The best performing compound Isorhamnetin achieved a value of -8.686 kcal/mol while other compounds achieved values between -8.652 and -5.522 kcal/mol, reflecting moderate to strong affinities. These results highlight the promise of AI-driven approaches in sustainable drug discovery that optimize resource utilization while minimizing experimental waste. Docking (animal) ; Flavonoid ; AutoDock ; Drug discovery ; Polyphenol ; Drug ; Ligand (biochemistry) ; Binding site Phytochemistry and Bioactive Compounds ; Phytochemistry and Biological Activities ; Morinda citrifolia extract uses
Authors: Junren Li | Luhua Lai
Source: arXiv (Cornell University) (preprint, 2025-12-23)
Field: Materials Science — Materials Chemistry

SynCraft: Guiding Large Language Models to Predict Edit Sequences for Molecular Synthesizability Optimization Generative artificial intelligence has revolutionized the exploration of chemical space, yet a critical bottleneck remains that a substantial fraction of generated molecules is synthetically inaccessible. Current solutions, such as post-hoc filtering or projection-based methods, often compromise structural novelty or disrupt key pharmacophores by forcing molecules into pre-defined synthetic templates. Herein, we introduce SynCraft, a reasoning-based framework that reframes synthesizability optimization not as a sequence translation task, but as a precise structural editing problem. Leveraging the emergent reasoning capabilities of Large Language Models, SynCraft navigates the "synthesis cliff" where minimal structural modifications yield significant gains in synthetic feasibility. By predicting executable sequences of atom-level edits rather than generating SMILES strings directly, SynCraft circumvents the syntactic fragility of LLMs while harnessing their chemical intuition. Extensive benchmarks demonstrate that SynCraft outperforms state-of-the-art baselines in generating synthesizable analogs with high structural fidelity. Furthermore, through interaction-aware prompting, SynCraft successfully replicates expert medicinal chemistry intuition in editing PLK1 inhibitors and rescuing high-scoring but previously discarded RIPK1 candidates in previous molecular generation literatures. Executable ; Key (lock) ; Generative grammar ; Language model ; Heuristics ; Intuition ; Bridging (networking) ; Pharmacophore Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Angel Hernandez-Mejias | Gabriel D. Jimenez-Nieves | Eduardo J. E. Caro‐Diaz
Source: ChemRxiv (preprint, 2025-04-02)
Field: Materials Science — Biomaterials

In Silico Design and Analysis of Cyanobacterial Pseudo-Natural Products Marine cyanobacteria give rise to natural products (NPs) that exhibit potent and selective bioactivity towards a broad spectrum of diseases. Nonetheless, like many other NPs, most of these compounds suffer from poor drug-like physicochemical properties while the percentage of structurally novel NPs steadily declines. To address this, we have created a library in silico of 2,415 cyanobacterial pseudo-NPs, where cyanobacterial NP fragments were tethered with privileged scaffolds from a variety of non-cyanobacterial NPs via a hypothetical peptide bond formation. Our pseudo-NP library was analyzed using various computational platforms to provide predicted physicochemical properties, lead-likeness penalties, NP-likeness scores, and Tanimoto coefficients when compared to publicly available compound libraries. Upon comparative analysis, our results demonstrate that most of the cyanobacterial pseudo-NPs created are predicted to possess drug- and lead-like properties, populate low density chemical space in terms of their NP-likeness, and provide structurally unique scaffolds when compared to publicly available small-molecule databases. We predict that our cyanobacterial pseudo-NP library will provide suitable synthetic targets for drug discovery and development, and a platform to be expanded upon using AI fragment harvesting tools for the creation of larger pseudo-NP libraries, creating synthetically tractable NP-like compound for drug discovery. In silico ; Natural (archaeology) ; Biochemical engineering ; Computational biology ; Computer science ; Biology ; Engineering ; Genetics Diatoms and Algae Research
Authors: Yicheng Yuan | Meifang Yang | Xinyi Lin | Wenguang Li | Zhenquan Yang | Guoxing Liu | Xu Qin | Yuxin Chen | Huan Pang | Tian Tian
Source: Supramolecular Materials (article, 2025-10-25)
Field: Materials Science — Materials Chemistry

Supramolecular engineering of luminescent materials: From molecular design to optoelectronic applications Luminescent materials often face trade-offs between efficiency, stability, and dynamic responsiveness, which limit their performance in advanced optoelectronic devices and bioimaging. Supramolecular chemistry offers a strategic solution by precisely modulating non-covalent interactions, enabling assemblies to suppress vibrational relaxation via conformational locking, enhance intersystem crossing through triplet-level tuning, and optimize energy transfer, thereby improving quantum yields and emission lifetimes. This review highlights synergistic supramolecular mechanisms that bridge molecular design and photophysical regulation across perylene bisimide materials, aggregation-induced emission luminogens, perovskite materials, room-temperature phosphorescence materials, and organic nonlinear optical materials, alongside their applications in luminescent displays, bioimaging, and information encryption. Despite notable advances, biocompatibility and signal stability remain challenging. AI and biohybrid approaches offer pathways toward programmable emission control. Schematic illustration of supramolecular engineering strategies for designing high-performance luminescent materials and their interdisciplinary applications in high-definition displays, in vivo imaging, and dynamic information encryption. Supramolecular chemistry ; Luminescence ; Phosphorescence ; Intersystem crossing ; Perylene ; Schematic ; Molecular engineering ; Light emission Luminescence and Fluorescent Materials ; Molecular Sensors and Ion Detection ; Supramolecular Self-Assembly in Materials