jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tobias Vornholt | Mojmír Mutný | Gregor W. Schmidt | Christian Schellhaas | Ryo Tachibana | Sven Panke | Thomas R. Ward | Andreas Krause | Markus Jeschek
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-02-06)
Field: Materials Science — Materials Chemistry

Enhanced Sequence-Activity Mapping and Evolution of Artificial Metalloenzymes by Active Learning Abstract Tailored enzymes hold great potential to accelerate the transition to a sustainable bioeconomy. Yet, enzyme engineering remains challenging as it relies largely on serendipity and is, therefore, highly laborious and prone to failure. The efficiency and success rates of engineering campaigns may be improved substantially by applying machine learning to construct a comprehensive representation of the sequence-activity landscape from small sets of experimental data. However, it often proves challenging to reliably model a large protein sequence space while keeping the experimental effort tractable. To address this challenge, we present an integrated pipeline combining large-scale screening with active machine learning and model-guided library design. We applied this strategy to efficiently engineer an artificial metalloenzyme (ArM) catalysing a new-to-nature hydroamination reaction. By combining lab automation and next-generation sequencing, we acquired sequence-activity data for several thousand ArM variants. We then used Gaussian process regression to model the activity landscape and guide further screening rounds according to user-defined objectives. Crucial characteristics of our enhanced enzyme engineering pipeline include i) the cost-effective generation of information-rich experimental data sets, ii) the integration of an explorative round to improve the performance of the model, as well as iii) the consideration of experimental noise during modelling. Our approach led to an order-of-magnitude boost in the hit rate of screening while making efficient use of experimental resources. Smart search strategies like this should find broad utility in enzyme engineering and accelerate the development of novel biocatalysts. Pipeline (software) ; Computer science ; Artificial intelligence ; Sequence space ; Machine learning ; Sequence (biology) ; Active learning (machine learning) Machine Learning in Materials Science ; Enzyme Catalysis and Immobilization ; Advanced biosensing and bioanalysis techniques
Authors: Shoujie Li | Yan Huang | Changqing Guo | Tong Wu | Jiawei Zhang | Linrui Zhang | Wenbo Ding
Source: arXiv (Cornell University) (preprint, 2024-06-12)
Field: Chemistry — Physical and Theoretical Chemistry

Chemistry3D: Robotic Interaction Benchmark for Chemistry Experiments The advent of simulation engines has revolutionized learning and operational efficiency for robots, offering cost-effective and swift pipelines. However, the lack of a universal simulation platform tailored for chemical scenarios impedes progress in robotic manipulation and visualization of reaction processes. Addressing this void, we present Chemistry3D, an innovative toolkit that integrates extensive chemical and robotic knowledge. Chemistry3D not only enables robots to perform chemical experiments but also provides real-time visualization of temperature, color, and pH changes during reactions. Built on the NVIDIA Omniverse platform, Chemistry3D offers interfaces for robot operation, visual inspection, and liquid flow control, facilitating the simulation of special objects such as liquids and transparent entities. Leveraging this toolkit, we have devised RL tasks, object detection, and robot operation scenarios. Additionally, to discern disparities between the rendering engine and the real world, we conducted transparent object detection experiments using Sim2Real, validating the toolkit's exceptional simulation performance. The source code is available at https://github.com/huangyan28/Chemistry3D, and a related tutorial can be found at https://www.omni-chemistry.com. Benchmark (surveying) ; Computer science ; Chemistry ; Artificial intelligence ; Geography ; Cartography Various Chemistry Research Topics ; Chemical Safety and Risk Management ; Biomedical and Engineering Education
Authors: Daniel York | Isaac Vidal | Cristina Segura | José Norambuena-Contreras | Francisco J. Martín‐Martínez
Source: Digital Discovery (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Data-driven representative models to accelerate scaled-up atomistic simulations of bitumen and biobased complex fluids Automated generation of data driven representative models of bitumen-like materials for large scale atomistic simulations. Asphalt ; Computer science ; Environmental science ; Materials science Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Phase Equilibria and Thermodynamics
Authors: Zhuo Diao | Hayato Yamashita | Masayuki Abe
Source: arXiv (Cornell University) (preprint, 2024-05-24)
Field: Materials Science — Materials Chemistry

Leveraging Large Language Models and Social Media for Automation in Scanning Probe Microscopy We present the development of an automated scanning probe microscopy (SPM) measurement system using an advanced large-scale language model (LLM). This SPM system can receive instructions via social networking services (SNS), and the integration of SNS and LLMs enables real-time, language-agnostic control of SPM operations, thereby improving accessibility and efficiency. The integration of LLMs with AI systems with specialized functions brings the realization of self-driving labs closer. Social media ; Automation ; Microscopy ; Computer science ; Natural language processing ; Optics ; World Wide Web ; Engineering ; Physics ; Mechanical engineering Machine Learning in Materials Science
Authors: Wenxin Lu | Haote Li | Jan Paul Menzel | Abbigayle E. Cuomo | Andrea Nikolić | H. Ray Kelly | Yu Shee | Sanil Sreekumar | Frédéric G. Buono | Jinhua J. Song | Robert H. Crabtree | Víctor S. Batista | Timothy R. Newhouse
Source: ChemRxiv (preprint, 2024-02-29)
Field: Chemistry — Inorganic Chemistry

Enhanced Ligand Discovery through Generative AI and Latent-Space Exploration: Application to the Mizoroki-Heck Reaction The identification of catalysts that promote chemical reactions is a critical challenge in the production of pharmaceuticals. One of the main bottlenecks in this process is the synthesis of vast libraries of precatalysts, although assessing catalyst effectiveness can be rapidly conducted through high-throughput experimentation. The rational design and development of high-performing precatalysts can circumvent this challenge and lead to important advances. In this study, we apply the transformer-based Kernel-Elastic Autoencoder (KAE) equipped with a conditioned latent space, enabling the targeted generation of ligands with desired steric and electronic properties. Our KAE model has facilitated the identification of a monodentate alkynylphosphine, dubbed MachinePhos A, as an effective precatalyst for forming carbon-carbon bond. Its utility was demonstrated experimentally in the Mizoroki-Heck reaction, using a variety of nitrogen-rich arenes pertinent to pharmaceutical applications. Chemical space ; Biochemical engineering ; Chemistry ; Steric effects ; Combinatorial chemistry ; Computer science ; Organic chemistry ; Drug discovery ; Engineering Asymmetric Hydrogenation and Catalysis ; Innovative Microfluidic and Catalytic Techniques Innovation ; Chemical Synthesis and Analysis
Authors: Sana Qausain | Mohd Basheeruddin
Source: Cureus (review, 2024-09-22)
Field: Materials Science — Materials Chemistry

Therapeutic Applications of Azo Dye Reduction: Insights From Methyl Orange Degradation for Biomedical Innovations This paper emphasizes the possible application of methyl orange reduction as a therapeutic technique, highlighting the potential of azo dye reduction in biomedical fields. The generally used azo dyes are toxic and carcinogenic; hence, they implicitly threaten the environment and health. The degradation of methyl orange, a famous example of azo dyes, is used to describe the degradation process for other azo dyes. This work discusses the ability of different methyl orange degradation methods, focusing on biocatalysts and nanomaterials, among the methods that identified enzymatic degradation with azoreductase enzymes as the method that quickly breaks down azo dyes under mild conditions as the most appropriate method, as well as its specificity as environmentally friendly. Moreover, metal nanoparticles such as silver and gold impellers increase the reducing efficiency because they offer a pivotal surface for the reduction reactions that undergo electron transfer. The complete breakdown of methyl orange is essential in biomedical usage. The strategies for treating azo dye reduction can be extended to next-generation drug delivery systems (DDS), biosensors, and therapeutic agents. Organisms involved in degradation can be functionalized to selectively degrade specific cells or tissues, thus presenting a new targeted therapy. Knowledge of degradation pathways and non-toxic products is essential in creating programs that build better and more efficient therapeutic agents. This work endeavors to illustrate the development of enzymatic and nanomaterials-based approaches to achieve sustainable azo dye decolorisation to open the gateway to developing other biomedical applications that tend to promote environmental and health-friendly solutions. Medicine ; Orange (colour) ; Degradation (telecommunications) ; Reduction (mathematics) ; Methyl orange ; Agent Orange ; Food science ; Biochemistry ; Photocatalysis ; Telecommunications Advanced Nanomaterials in Catalysis ; Dye analysis and toxicity ; Nanoparticles: synthesis and applications
Authors: Jike Wang | Rui Qin | Mingyang Wang | Meijing Fang | Yangyang Zhang | Yuchen Zhu | Qun Su | Qiaolin Gou | Chao Shen | Odin Zhang | Zhenxing Wu | Dejun Jiang | Xujun Zhang | Huifeng Zhao | Xiaozhe Wan | Zhourui Wu | Liwei Liu | Yu Kang | Chang‐Yu Hsieh | Tingjun Hou
Source: arXiv (Cornell University) (preprint, 2024-07-10)
Field: Materials Science — Materials Chemistry

Token-Mol 1.0: Tokenized drug design with large language model Significant interests have recently risen in leveraging sequence-based large language models (LLMs) for drug design. However, most current applications of LLMs in drug discovery lack the ability to comprehend three-dimensional (3D) structures, thereby limiting their effectiveness in tasks that explicitly involve molecular conformations. In this study, we introduced Token-Mol, a token-only 3D drug design model. This model encodes all molecular information, including 2D and 3D structures, as well as molecular property data, into tokens, which transforms classification and regression tasks in drug discovery into probabilistic prediction problems, thereby enabling learning through a unified paradigm. Token-Mol is built on the transformer decoder architecture and trained using random causal masking techniques. Additionally, we proposed the Gaussian cross-entropy (GCE) loss function to overcome the challenges in regression tasks, significantly enhancing the capacity of LLMs to learn continuous numerical values. Through a combination of fine-tuning and reinforcement learning (RL), Token-Mol achieves performance comparable to or surpassing existing task-specific methods across various downstream tasks, including pocket-based molecular generation, conformation generation, and molecular property prediction. Compared to existing molecular pre-trained models, Token-Mol exhibits superior proficiency in handling a wider range of downstream tasks essential for drug design. Notably, our approach improves regression task accuracy by approximately 30% compared to similar token-only methods. Token-Mol overcomes the precision limitations of token-only models and has the potential to integrate seamlessly with general models such as ChatGPT, paving the way for the development of a universal artificial intelligence drug design model that facilitates rapid and high-quality drug design by experts. Drug ; Computer science ; Medicine ; Pharmacology Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Haotian Chen | Minjun Yang | Bedřich Smetana | Vlastimil Novák | Vlastimil Matějka | Richard G. Compton
Source: Angewandte Chemie (article, 2024-01-05)
Field: Materials Science — Materials Chemistry

Discovering Electrochemistry with an Electrochemistry‐Informed Neural Network (ECINN) Abstract Machine learning is increasingly integrated into chemistry research by guiding experimental procedures, correlating structure and function, interpreting large experimental datasets, to distill scientific insights that might be challenging with traditional methods. Such applications, however, largely focus on gaining insights via big data and/or big computation, while neglecting the valuable chemical prior knowledge dwelling in chemists’ minds. In this paper, we introduce an Electrochemistry‐Informed Neural Network (ECINN) by explicitly embedding electrochemistry priors including the Butler–Volmer (BV), Nernst and diffusion equations on the backbone of neural networks for multi‐task discovery of electrochemistry parameters. We applied the ECINN to voltammetry experiments of and redox couples to discover electrode kinetics and mass transport parameters. Notably, ECINN seamlessly integrated mass transport with BV to analyze the entire voltammogram to infer transfer coefficients directly, so offering a new approach to Tafel analysis by outdating various mass transport correction methods. In addition, ECINN can help discover the nature of electron transfer and is shown to refute incorrect physics if imposed. This work encourages chemists to embed their domain knowledge into machine learning models to start a new paradigm of chemistry‐informed machine learning for better accountability, interpretability, and generalization. Interpretability ; Computer science ; Artificial neural network ; Artificial intelligence ; Tafel equation ; Chemistry ; Machine learning ; Electrochemistry ; Electrode ; Physical chemistry Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Computational Drug Discovery Methods
Authors: Mohan Raparthi
Source: Dandao Xuebao/Journal of Ballistics (article, 2024-01-10)
Field: Materials Science — Materials Chemistry

Investigating the use of Deep Learning, in Materials Research for Predicting Material Properties, Identifying new Materials, and Optimizing Material Selection for Mechanical Components The rapid advancements in deep learning techniques have spurred a paradigm shift in materials research, revolutionizing the way we predict material properties, identify novel materials, and optimize material selection for mechanical components. This paper explores the integration of deep learning methodologies into materials science, presenting a comprehensive investigation into their efficacy and potential applications. The paper explores the development of deep learning models for predicting material properties.[1] Leveraging vast datasets containing information on diverse materials and their corresponding properties, we delve into the application of neural networks to establish robust predictive models. By extracting complex relationships within the data, deep learning facilitates the accurate estimation of material characteristics, enabling researchers and engineers to streamline the materials discovery process. In addition to property prediction, the study explores the role of deep learning in the identification of new materials with superior or tailored attributes. By training models on extensive databases encompassing known materials and their functionalities, we investigate the ability of deep learning algorithms to suggest novel materials with specific desired properties. This capability holds immense promise for accelerating the discovery of innovative materials, especially in fields where tailored material performance is critical. Furthermore, the paper examines the utilization of deep learning in optimizing material selection for mechanical components. By considering a holistic approach that factors in mechanical, thermal, and other relevant properties, we explore how neural networks can assist in selecting the most suitable materials for specific applications. This not only enhances the efficiency of the design process but also contributes to the development of more durable, efficient, and sustainable mechanical components. Through a systematic exploration of the integration of deep learning in materials research, this paper provides valuable insights into the transformative potential of these techniques. The findings contribute to the ongoing discourse on the intersection of artificial intelligence and materials science, paving the way for accelerated advancements in materials discovery, design, and application. Deep learning ; Computer science ; Artificial intelligence ; Process (computing) ; Machine learning ; Material selection ; Selection (genetic algorithm) ; Identification (biology) ; Artificial neural network ; Property (philosophy) ; Materials informatics ; Materials science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Material Selection and Properties
Authors: Norberto A. Guzman | Andrea Guzman
Source: Preprints.org (preprint, 2024-04-29)
Field: Chemistry — Spectroscopy

Human Sputum Proteomics: Advancing Non-Invasive Diagnosis of Respiratory Diseases with Enhanced Biomarker Analysis Methods Many ailments can be diagnosed while they are asymptomatic, meaning that the patient has no signs or symptoms of a progressing disease. If caught in their initial stage of formation, these maladies can be effectively treated, leading to successful outcomes; curative therapies can halt illnesses from advancing, thus improving the quality of life, and long-term survival of the patient. Still cutting-edge upgrades in precision technologies are necessary for early, reliable, affordable, and rapid disease detection, but also are vital for the well-being of people and the future of global public health. The emerging role and utility of non-invasive and repeatable diagnostic test approaches for detection of health conditions has been liquid biopsies based on genomic biomarkers. As such, biological fluids permit any measurable molecular indicator or signature (e.g., proteins), to provide valuable information on individual&amp;rsquo;s wellness and/or disease. Among the bodily secretions used for non-invasive diagnostics is sputum, a complex viscous gel-like biopolymeric network, that has gained growing recognition as a rich source of biomarkers of airway infections, pulmonary diseases, and serves as a determinant to reveal other illnesses. As per the World Health Organization, the burden of respiratory conditions is exacerbated by factors, ranging from considerable subjection to air pollution and occupational contaminants, to tobacco smoking and second-hand smoke, in addition to poor socio-economic status. Due to the likely increase of these determinants, respiratory tract ailments are on the rise, affecting the health of many individuals, in addition to putting stress on healthcare facilities and services worldwide. We therefore highlight the need to use expectorated or induced sputum specimens as a routine sample source for testing valuable protein biomarkers to diagnose these chronic maladies, to predict inflammation and disease progression, as well as monitor the effectiveness of treatments. Further, we discuss the urgency for fast and reliable point-of-care methods to detect and quantify crucial protein biomarkers in sputum specimens, and limitations faced when dealing with their complex matrices. Sputum ; Biomarker ; Proteomics ; Medicine ; Biomarker discovery ; Respiratory system ; Computational biology ; Pathology ; Biology ; Internal medicine ; Tuberculosis Advanced Proteomics Techniques and Applications ; Respiratory viral infections research ; Advanced Biosensing Techniques and Applications
Authors: Archana Archana | Abha Awasthi | Sakshi Chaudhary
Source: Oriental Journal Of Chemistry (article, 2024-02-25)
Field: Chemistry — Organic Chemistry

Synthesis of Novel Indolylbenzothiazepines/Indolylbenzoxaziepines Substituted 2-Oxo/Thiobarbituric Acids as Potential Anticonvulsant Agents. 4-(2’-Oxo/thiobarbiturinyl acid) – 2 - (2”-halo-1”H-indolyl) - 2,3 - dihydro - -1,5-benzothiazepines (7-10) and 4-(2’-oxo/thiobarbiturinyl acid) – 2 - (2”-halo-1”H-indolyl) - 2,3 - dihydro - -1,5-benzoxazepines (11-14) undergoes Mannich reaction to afford compounds 4-(2’-oxo/thiobarbiturinyl acid) – 2 - (2”-halo-1”H-indolyl) – 3 - (substitutedphenyl aminomethylene) - 2,3 - dihydro - 1,5-benzothiazepines (15-22) and 4-(2’-oxo/thiobarbiturinyl acid) – 2 - (2”-halo-1”H-indolyl) – 3-( substitutedphenyl aminomethylene) - 2,3 - dihydro - 1,5-benzoxazepines (23-30) correspondingly. All the chemical framework of these newer drugs were elucidated by using elemental and IR and NMR spectroscopy. All these newly synthesized compounds were tested for antiepileptic effect against SMES experimental models and the results were collated with phenytoin sodium - standard drug. Results of antiepileptic profile showed promising effect in most of the derivatives synthesized. Activity equal to standard drug was shown by compounds 9 and 28. The most promising and active compound of this project was found to be 4-(2’- thiobarbiturinyl acid) – 2 - (2”-chloro-1”H-indolyl) – 3 - (chlorophenyl aminomethylene) - 2,3 - dihydro - 1,5-benzothiazepines, which elicited activity greater than the standard drug. All the antiepileptic drugs of the produced in this projects were also tested for ALD50. Elsevier Biobase ; Material chemistry ; Chemistry ; Green chemistry ; Industrial chemistry ; Food chemistry ; FLUIDEX ; GEOBASE ; Phytochemistry ; Anticonvulsant ; Evisa ; Theoretical chemistry ; Thiobarbituric acid ; Organic chemistry ; Pharmacology ; Supramolecular chemistry ; Antioxidant ; Biochemical engineering ; Psychology ; Medicine ; Epilepsy ; Neuroscience ; Crystal structure ; Lipid peroxidation Multicomponent Synthesis of Heterocycles ; Synthesis and pharmacology of benzodiazepine derivatives ; Phenothiazines and Benzothiazines Synthesis and Activities