jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sudarshan Behera (8654595) | David F. Hahn (9681119) | Carter J. Wilson (10661444) | Simone Marsili (750469) | Gary Tresadern (836509) | Vytautas Gapsys (222335) | Bert L. de Groot (7210289)
Source: Figshare (article, 2025-06-30)
Field: Materials Science — Materials Chemistry

Quantificationof the Impact of Structure Qualityon Predicted Binding Free Energy Accuracy Relative binding free energy (RBFE) calculations have emerged as a powerful tool in drug discovery, capable of achieving experimental-level accuracy. However, the accuracy is compromised by a multitude of factors, including the initial structure modeling. The current study contributes to the quantification of the impact of initial structure modeling on the accuracy across a diverse set of activity cliff pairs. Along with providing a quantitative relation between the resolution of the crystal structure and free energy accuracy, we also demonstrate the incorporation of a secondary solvation tool (SOLVATE) to increase the free energy accuracy, especially when crystal waters are missing. The study also evaluates the reliability of AI-predicted structures in RBFE calculations, showing their effectiveness in predicting RBFE directionality and assigning nominal resolutions to the predicted structures based on free energy accuracy. These findings provide a set of recommendations for the development of more robust RBFE protocols, informing the use of structural data, solvation techniques, and AI-predicted protein models in drug discovery. Solvation ; Reliability (semiconductor) ; Energy (signal processing) ; Quality (philosophy) ; Set (abstract data type) ; Data set Enzyme Structure and Function ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Mengwei Sun | Chengwei Ai | Diya Zhang | Xiaoyi Liu | Qiaozhen Meng | Shiqiang Ma | Fei Guo
Source:  (article, 2025-12-15)
Field: Materials Science — Materials Chemistry

MolInterAct: Multiscale Cross-Modal Interaction for Robust Molecular Representation Learning Molecular representation learning, which captures the fundamental characteristics of chemical compounds, is crucial for AI-driven drug discovery. Existing methods integrate various modalities (e.g., 2D topology and 3D geometry) to develop robust representations. However, current multi-modal fusion strategies either align embedding space through independent models separately, thereby overlooking complementary information, or bridge modalities at a coarse-grained level, failing to capture inherent correlations. We present MolInterAct, an innovative pretraining framework designed to promote multiscale interactions between 2D and 3D modalities at both atomic-level and moleculelevel. Specifically, we propose a fine-grained fusion module, coupled with a customized complementary masking strategy, to seamlessly integrate information at the atomic-level, mitigating overlap and similarity between 2D and 3D representations. In addition, we introduce a fusion contrastive module, which operates at the molecule level, to further strengthen the fusion of 2D and 3D representations while preserving modality-specific features. Finally, we incorporate an intra-modal reconstruction module to reconstruct the original information, further refining the model's understanding of individual modality. Extensive experiments demonstrate that our model outperforms existing molecular pretraining methods across both 2D and 3D benchmarks, highlighting the effectiveness of multiscale fusion between modalities. Representation (politics) ; Embedding ; Modalities ; Masking (illustration) ; Fusion ; Sensor fusion ; Similarity (geometry) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Michał Markiewicz | Michał Gucwa | Jerzy Bazak | Wojciech Dec | Wladek Minor | Krzysztof Murzyn
Source: Expert Opinion on Drug Discovery (article, 2025-12-26)
Field: Materials Science — Materials Chemistry

Enhancing structural insights for advanced drug discovery by mitigating protein crystal damage To advance drug discovery, the structural biology community must adopt unified standards for data validation and experimental documentation. High-quality, reproducible structures are essential for minimizing artifacts and supporting AI-driven modeling and screening. A coordinated effort to integrate damage-aware practices and metadata standards will enhance the fidelity of structural data and its utility in therapeutic innovation. Drug discovery ; Metadata ; Fidelity ; Structural biology ; Drug ; Drug development ; Systems biology ; Chemical biology Enzyme Structure and Function ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Mikhail G. Andronov | Natalia Andronova | Michael Wand | Jürgen Schmidhuber | Djork-Arné Clevert
Source: ArXiv.org (preprint, 2025-08-02)
Field: Materials Science — Materials Chemistry

Fast and scalable retrosynthetic planning with a transformer neural network and speculative beam search AI-based computer-aided synthesis planning (CASP) systems are in demand as components of AI-driven drug discovery workflows. However, the high latency of such CASP systems limits their utility for high-throughput synthesizability screening in de novo drug design. We propose a method for accelerating multi-step synthesis planning systems that rely on SMILES-to-SMILES transformers as single-step retrosynthesis models. Our approach reduces the latency of SMILES-to-SMILES transformers powering multi-step synthesis planning in AiZynthFinder through speculative beam search combined with a scalable drafting strategy called Medusa. Replacing standard beam search with our approach allows the CASP system to solve 26\% to 86\% more molecules under the same time constraints of several seconds. Our method brings AI-based CASP systems closer to meeting the strict latency requirements of high-throughput synthesizability screening and improving general user experience. CASP ; Scalability ; Retrosynthetic analysis ; Transformer ; Latency (audio) ; Beam search Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Chengcheng Sun | Peng Gu | Hailang Wan | Mingjun Ren
Source: Applied Surface Science (article, 2025-11-22)
Field: Materials Science — Surfaces, Coatings and Films

AI-driven design of tunable wettability on composite surfaces Wetting ; Contact angle ; Microscale chemistry ; Microstructure ; Composite number ; Surface energy Surface Modification and Superhydrophobicity ; Fluid Dynamics and Thin Films ; Polymer Surface Interaction Studies
Authors: Qianhuiwen Hu | Yiming Li | Jun Liu | Feiyang Wang | Chengcheng Jiang | Ziwei Huang | Dewu Lin | Zhaolong Chen
Source: Precision Chemistry (article, 2025-12-23)
Field: Materials Science — Materials Chemistry

Emergence of Artificial Intelligence in the Structure-Controllable Synthesis of Carbon Nanotubes Carbon nanotubes (CNTs) exhibit remarkable properties that have spurred extensive exploration across domains such as integrated circuits, aerospace, and energy storage. With application scenarios becoming increasingly specialized, the structure-controllable synthesis of CNTs faces escalating challenges. This review summarizes chemical vapor deposition (CVD) techniques for controlled CNT synthesis and examines the structural-control mechanisms during growth, emphasizing the critical factors influencing CNT diameter, electronic properties, and chirality. Conventional trial-and-error approaches have become inadequate in addressing the demands for precise structural manipulation during synthesis and complex variable optimization in scaled production. Recently, artificial intelligence (AI) has substantially advanced scientific research and technological innovation. In the concluding perspective, we highlight emerging paradigms that incorporate AI into CNT synthesis, where the synergy between data-driven experimentation and physics-informed constraints may enable the development of accurate and efficient digital twins of CNT growth systems. Such approaches offer promise for the inverse design of synthesis routes and deeper insight into structure-control mechanisms. We conclude by identifying promising directions for AI-enhanced CNT synthesis, including multiscale computational simulations, catalyst design, automated experimental platforms, and pilot-scale production, which may collectively advance the Frontier of precision nanomanufacturing. Carbon nanotube ; Applications of artificial intelligence Carbon Nanotubes in Composites ; Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Setareh Sheikh | V. Subhasree Navya | Riya Sharma | Sanjoy Roy | Jayant K. Singh
Source: ArXiv.org (preprint, 2025-11-29)
Field: Chemistry — Organic Chemistry

Digital Surfactant Surfactants play an important role in determining the cleaning performance and stability of detergents. However, the design of new surfactants using traditional methods is often time-consuming, complex, and largely based on trial and error. Recent studies have incorporated data-driven and computational approaches to generate new surfactants and predict properties of surfactants, but most of these approaches either optimize on a single property or train on a small number of surfactants. In this work, we investigate the generative capabilities of an existing graph diffusion based inverse design model and a transformer based molecule optimization model, for non-ionic surfactants. We train both models to generate non-ionic surfactants based on single- and multi-property values, predict the same properties o using trained property predictor models for generated molecules, and validate a few them using molecular dynamics simulations. Our results reveal that the inverse design model is better at generating a diverse set of molecules, while the transformer is better at generating molecules which satisfy input property constraints better. We also observe that molecules generated using single property condition, on average, satisfy the input property condition better when compared to molecules generated using multiple property conditions. From molecular dynamics simulations, we observe that the predicted properties of the selected molecules are close to the simulated results concluding that both methods are capable of generating surfactants that actually satisfy input property condtions. Property (philosophy) ; Inverse ; Molecule ; Set (abstract data type) ; Inverse problem ; Transformer Advanced Polymer Synthesis and Characterization ; Block Copolymer Self-Assembly ; Surfactants and Colloidal Systems
Authors: Vinícius de Almeida Paiva | Douglas E. V. Pires | Gustavo Costa Bressan | Sandro Carvalho Izidoro | Sabrina de Azevedo Silveira
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-02-23)
Field: Chemistry — Spectroscopy

BENDER DB: a database of protein binding sites across neglected disease proteomes Abstract Identifying binding sites is crucial for expanding our knowledge of various biological processes, supporting drug discovery, and repositioning strategies, particularly in the early research phases of target hopping. This is especially important for neglected diseases, which predominantly impact vulnerable populations in developing regions. To address this, we created BENDER DB, a database designed to map predicted protein binding sites within the proteomes of pathogens associated with neglected diseases. Utilizing AlphaFold-predicted structures, BENDER DB integrates results from five leading binding site prediction tools, resulting in over one million binding sites across over 100,000 proteins from 10 different proteomes. BENDER DB offers unique features, such as integrating multiple predictors, interactive visualization tools, and comprehensive graphical representations, allowing for a detailed visual comparative analysis of different prediction methods and the predicted binding sites. We combined the computational approaches to offer a consensus of binding site predictions, leveraging the strengths of multiple techniques. Additionally, we introduce BENDER AI, a meta-predictor that combines the outputs of the predictors to provide a unified binding site classification. BENDER AI outperformed all individual methods in 4 out of 5 evaluated metrics. Specifically, it achieved an MCC of 0.64 and an AUC of 0.89, surpassing the highest MCC of 0.63 and the highest AUC of 0.84 obtained by individual predictors, demonstrating strong performance and effectiveness in supporting binding site prediction. By consolidating these results, BENDER DB enables detailed comparative analyses of prediction methods and integrates interactive visualization tools to provide users with an intuitive platform for binding site exploration. The database aims to accelerate drug discovery efforts, particularly in underdeveloped regions, by providing a robust and detailed resource for studying protein binding sites. BENDER DB is available at https://benderdb.ufv.br . Proteome ; Computational biology ; Database ; Computer science ; Data science ; Biology ; Bioinformatics Advanced Proteomics Techniques and Applications ; Bioinformatics and Genomic Networks
Authors: Chenghao Zhu | Weiping Ding | Wen Zhang | Rui Zhou | Zhaohong Deng
Source:  (article, 2025-12-15)
Field: Materials Science — Materials Chemistry

ADKcat: Enhanced Enzyme Turnover Number Prediction with Adaptive Data Augmentation and Dual Information Exploration The enzyme turnover number is a key metric for catalytic efficiency. While recent deep learning models have integrated enzyme and substrate modal information for predicting turnover numbers, the following challenges remain. First, existing datasets are limited in scale, inconsistent, and lack standardization. Second, current models emphasize consistency information across modalities while ignoring the specific information within each modality. Third, the imbalanced distribution of measured turnover numbers leads to poor performance of existing methods in extreme value ranges. To address these challenges, we first construct Kinetic-DB, a large, standardized dataset compiled from public sources. Based on this, we propose a novel method, ADKcat, for enzyme turnover number prediction. Specifically, to mitigate the challenge of imbalanced data, an adaptive data augmentation module is first constructed to enrich both enzyme and substrate sequences. Then, two pretrained language models-ESM-2 for enzymes and Mole-BERT for substrates-are used for embedding extraction. Furthermore, to fully explore common and specific information, we introduce a dual information exploration module and enhance it with domain classification and distribution alignment loss functions. Finally, an adaptive density weighted network is further applied to improve prediction accuracy and robustness. Experiments show that ADKcat outperforms state-of-the-art methods, especially in extreme turnover ranges. This makes it a promising tool for enzyme engineering, drug discovery, and synthetic biology. Consistency (knowledge bases) ; Metric (unit) ; Embedding ; Dual (grammatical number) ; Domain adaptation ; Domain (mathematical analysis) ; Term (time) ; Key (lock) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Bioinformatics and Genomic Networks
Authors: Feng-Wei Guo (17708912) | Xiangyun Kong (8749083) | Mengya Li (1781854) | Jing Wang (6206297) | Baohua Zhang (521406) | Xiang-Ping Kong (11652967) | Fangzhi Hu (6280568) | Wishwajith Kandegama (17135725) | Fengyu Du (9131929) | Shuai-Shuai Li (1581274)
Source: Figshare (article, 2025-08-13)
Field: Chemistry — Organic Chemistry

Efficient Synthesisof 1′,4′-Dihydro-2′<i>H</i>‑spiro[indoline-3,3′-quinoline]Derivativesfor the Identification of New Spirocyclic Herbicidal Leads Spiro scaffolds are ubiquitous in natural products and drugs. Their special three-dimensional structure and biological activities have aroused wide interest. Among various spiro compounds, spiro-fused indoline derivatives represent a particularly interesting subset but have attracted less attention for drug discovery, especially in the field of pesticides. In this study, twenty-nine 1′,4′-dihydro-2′<i>H</i>-spiro­[indoline-3,3′-quinoline] derivatives were designed and efficiently synthesized via one-pot synthesis methods; their herbicidal activity was also evaluated. Most of the synthesized spiro-fused indoline derivatives exhibited considerable herbicidal activity against the growth of Echinochloa crusgalli seedlings. Notably, compounds <b>8a</b>, <b>8d</b>, and <b>8f</b> exhibited root and stem inhibition rates exceeding 95% and 85% at a dosage of 50 mg/L, respectively. Furthermore, a greenhouse experiment revealed that compound <b>8a</b> demonstrated postemergence herbicidal activities against E. crusgalli with a fresh-weight inhibition rate of 73.8% at a dosage of 400 g ai/ha, demonstrating superior activity compared with glyphosate (58.4%). Importantly, compound <b>8a</b> exhibited good crop selectivity against wheat, maize, and oilseed rape. Therefore, <b>8a</b> is a promising herbicidal candidate that is worthy of further development. Indoline ; Indole test ; Selectivity ; Drug ; Identification (biology) ; Structure–activity relationship Synthesis and biological activity ; Synthesis and bioactivity of alkaloids ; Multicomponent Synthesis of Heterocycles
Authors: Ann Boija | Bede Portz | Y. Ye | Jian-Guo Ren | Thomas F. Durand-Réville | Kip A. West | Isaac A. Klein
Source: Cancer Research (article, 2025-04-21)
Field: Materials Science — Biomaterials

Abstract 6987: MYC condensate-modulating small molecules demonstrate robust antitumor activity in animal models of ovarian cancer and other solid tumors Abstract The transcription factor MYC is a critical driver of cancer, implicated in 70% of malignancies and amplified in approximately 30% of ovarian cancers, where it contributes to aggressive tumor progression. Despite its pivotal role in oncogenesis, MYC has eluded traditional structure-based drug discovery efforts due to the absence of well-defined drug binding sites and its dependence on highly dynamic interactions. Here, we are deploying condensate biology and phenotypic screening to identify and optimize small molecule condensate modulators (c-mods) to modify MYC oncogenic function. In vitro, MYC c-mods dissolve MYC nuclear condensates, selectively repress MYC-driven gene expression, and induce cell killing in a spectrum of ovarian cancer and esophageal cancer cell lines. Advanced MYC c-mods optimized using Dewpoint’s AI/ML-powered end-to-end platform are orally bioavailable and yield tumor stasis and regression in in vivo models of ovarian cancer at tolerated daily doses. We demonstrate that the antitumor activity of the MYC c-mods extends to additional in vivo models from multiple indications and correlates with repressed MYC activity in tumors. Citation Format: Ann Boija, Bede Portz, Yingda Ye, Jian-Guo Ren, Thomas Durand-Reville, Kip West, Isaac Klein. MYC condensate-modulating small molecules demonstrate robust antitumor activity in animal models of ovarian cancer and other solid tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6987. Ovarian cancer ; Cancer research ; Solid tumor ; Cancer ; Biology ; Medicine ; Internal medicine Nanoparticle-Based Drug Delivery
Authors: D. K. S. Lekshmi | Arsha S Nair | A. Jayakrishnan | Sandhya KS
Source: ChemistrySelect (article, 2025-06-28)
Field: Materials Science — Biomaterials

A Computational Study of Doxorubicin Analogues Conjugated To Polysorbate 80 for Targeted Delivery to the Brain Tumor Abstract Drug discovery for the treatment of central nervous system (CNS) diseases is a highly complex affair, especially due to the blood brain barrier (BBB) restricting the entry of many drugs. In the present study, we utilized in silico studies to screen twelve derivatives of doxorubicin (DOX) against seven tumor proteins (3LGL,2IG0, 3LGF, 4RG2, 3PG7, 3LH0 and 4HBM). The DOX ligands were modelled and optimized using DFT, and the binding affinity was evaluated. Later, an attempt was made to model modified DOX ligands with polysorbate 80 (PS80). After categorizing 12 DOX ligands and 12 DOX‐PS80 conjugated ligands, the top‐ranked three DOX ligands ( R6 , R4 , and R9 ) are conjugated with PS80 ( R6‐PS80 , R4‐PS80, and R9‐PS80 ). The LE of the best docked system also showed the highest which indicates a minimal number of atoms for strong binding and is favorable for drug‐like properties. Total of six systems is underwent comprehensiveMD analysis. The best DOX ligands and their conjugated systems were subjected to a thorough examination for structural stability (RMSD), H‐bond, effect of SASA, and R g . The results demonstrated that all six systems exhibited folding behavior and were highly stable. Furthermore, we employed MM/GBSA to figure out realistic binding free energy of the top three DOX‐PS80 systems. Using the ADMET AI server it is shown that attaching to PS80 could potentially decrease the overall toxicity of DOX ligands. These findings indicate that the modified DOX ligands when combined with PS80, have the potential to cross the BBB and could be used for further in vitro and in vivo evaluation. Conjugated system ; Doxorubicin ; Brain tumor ; Chemistry ; Drug delivery ; Pharmacology ; Cancer research ; Medicine ; Internal medicine ; Pathology ; Chemotherapy ; Organic chemistry Nanoparticle-Based Drug Delivery ; Radiopharmaceutical Chemistry and Applications ; Computational Drug Discovery Methods
Authors: Rima Heider Al Omari | Réza Mohammadi | Subbulakshmi Ganesan | Hasnaawei Al‐Shaker | J. Karthikeyan | Renu Sharma | Aashna Sinha
Source: Scientific Reports (article, 2025-11-23)
Field: Chemistry — Organic Chemistry

Fe₃O₄@MCM-41-Cystine-ZnCl₂ nanocomposite in deep eutectic solvent: a sustainable and scalable catalytic system for three-component synthesis of triarylpyridines In this paper, we report a novel Fe₃O₄@MCM-41-Cystine-ZnCl₂ nanocatalyst as a highly active, recyclable, and sustainable platform for the synthesis of triarylpyridines. This catalyst integrates the benefits of a mesoporous MCM-41 support, magnetic Fe₃O₄ core, and ZnCl₂ Lewis acid centers stabilized by cystine linkers, resulting in high surface area, strong catalytic activity, and excellent stability. Under optimized conditions (100 °C, 2 h, 7 mol% catalyst) in a deep eutectic solvent (choline chloride-glycerol), twenty triarylpyridines were obtained in excellent yields (84-98%). The system exhibited broad substrate tolerance, efficiently converting electron-donating, electron-withdrawing, sterically hindered, and heteroaryl substrates with consistent turnover numbers (TON = 12.0-14.0) and turnover frequencies (TOF = 6.0-7.0 h⁻<sup>1</sup>). Compared to conventional catalysts, this method offers significantly shorter reaction times, higher yields, operation under air without oxidants, and facile magnetic recovery with reusability up to eight cycles. Thus, this approach provides a practical, eco-friendly, and scalable alternative for the synthesis of triarylpyridines, aligning efficiency with sustainability. Eutectic system ; Catalysis ; Reusability ; Nanocomposite ; Deep eutectic solvent ; Substrate (aquarium) ; Mesoporous material Multicomponent Synthesis of Heterocycles ; Chemical Synthesis and Reactions ; Catalytic Cross-Coupling Reactions