jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Phyo Phyo Kyaw Zin | Alexandre Borrel | Denis Fourches
Source: Journal of Chemical Information and Modeling (article, 2020-07-05)
Field: Chemistry — Organic Chemistry

Benchmarking 2D/3D/MD-QSAR Models for Imatinib Derivatives: How Far Can We Predict? Imatinib, a 2-phenylaminopyridine-based BCR-ABL tyrosine kinase inhibitor, is a highly effective drug for treating Chronic Myeloid Leukemia (CML). However, cases of drug resistance are constantly emerging due to various mutations in the ABL kinase domain; thus, it is crucial to identify novel bioactive analogues. Reliable QSAR models and molecular docking protocols have been shown to facilitate the discovery of new compounds from chemical libraries prior to experimental testing. However, as the vast majority of QSAR models strictly relies on 2D descriptors, the rise of 3D descriptors directly computed from molecular dynamics simulations offers new opportunities to potentially augment the reliability of QSAR models. Herein, we employed molecular docking and molecular dynamics on a large series of Imatinib derivatives and developed an ensemble of QSAR models relying on deep neural nets (DNN) and hybrid sets of 2D/3D/MD descriptors in order to predict the binding affinity and inhibition potencies of those compounds. Through rigorous validation tests, we showed that our DNN regression models achieved excellent external prediction performances for the pKi data set (n = 555, R<sup>2</sup> ≥ 0.71. and MAE ≤ 0.85), and the pIC<sub>50</sub> data set (n = 306, R<sup>2</sup> ≥ 0.54. and MAE ≤ 0.71) with strict validation protocols based on external test sets and 10-fold native and nested cross validations. Interestingly, the best DNN and random forest models performed similarly across all descriptor sets. In fact, for this particular series of compounds, our external test results suggest that incorporating additional 3D protein-ligand binding site fingerprint, descriptors, or even MD time-series descriptors did not significantly improve the overall R<sup>2</sup> but lowered the MAE of DNN QSAR models. Those augmented models could still help in identifying and understanding the key dynamic protein-ligand interactions to be optimized for further molecular design. Quantitative structure–activity relationship ; Imatinib ; Molecular descriptor ; Test set ; Computational biology ; Docking (animal) ; Computer science ; Artificial intelligence ; Chemistry ; Machine learning ; Myeloid leukemia ; Biology ; Medicine Click Chemistry and Applications ; Computational Drug Discovery Methods ; Chronic Myeloid Leukemia Treatments
Authors: Seiji Takeda | Toshiyuki Hama | Hsiang-Han Hsu | Victoria A. Piunova | Dmitry Yu. Zubarev | Daniel P. Sanders | Jed W. Pitera | Makoto Kogoh | Takumi Hongo | Yenwei Cheng | Wolf Bocanett | Hideaki Nakashika | Akihiro Fujita | Tsuchiya Yuta | Katsuhiko Hino | Kentaro Yano | Shuichi Hirose | Hiroki Toda | Yasumitsu Orii | Daiju Nakano
Source:  (article, 2020-08-20)
Field: Materials Science — Materials Chemistry

Molecular Inverse-Design Platform for Material Industries The discovery of new materials has been the essential force which brings a discontinuous improvement to industrial products' performance. However, the extra-vast combinatorial design space of material structures exceeds human experts' capability to explore all, thereby hampering material development. In this paper, we present a material industry-oriented web platform of an AI-driven molecular inverse-design system, which automatically designs brand new molecular structures rapidly and diversely. Different from existing inverse-design solutions, in this system, the combination of substructure-based feature encoding and molecular graph generation algorithms allows a user to gain high-speed, interpretable, and customizable design process. Also, a hierarchical data structure and user-oriented UI provide a flexible and intuitive workflow. The system is deployed on IBM's and our client's cloud servers and has been used by 5 partner companies. To illustrate actual industrial use cases, we exhibit inverse-design of sugar and dye molecules, that were carried out by experimental chemists in those client companies. Compared to a general human chemist's standard performance, the molecular design speed was accelerated more than 10 times, and greatly increased variety was observed in the inverse-designed molecules without loss of chemical realism. Inverse ; Computer science ; Mathematics ; Geometry Machine Learning in Materials Science ; Nanowire Synthesis and Applications ; Advanced Materials Characterization Techniques
Authors: Qianhui Liu | Shuhao Yang | H.H. Repich | Yixuan Zhai | Xiaosa Xu | Yeru Liang | Hejun Li | Hongqiang Wang | Fei Xu
Source: Frontiers in Chemistry (article, 2020-12-02)
Field: Materials Science — Materials Chemistry

Porous Functionalized Covalent-Triazine Frameworks for Enhanced Adsorption Toward Polysulfides in Li-S Batteries and Organic Dyes The incorporation of functional building blocks to construct functionalized and highly porous covalent triazine frameworks (CTFs) is essential to the emerging adsorptive-involved field. Herein, a series of amide functionalized CTFs (CTF-PO71) have been synthesized using a bottom-up strategy in which pigment PO71 with an amide group is employed as a monomer under ionothermal conditions with ZnCl<sub>2</sub> as the solvent and catalyst. The pore structure can be controlled by the amount of ZnCl<sub>2</sub> to monomer ratio. Benefitting from the highly porous structure and amide functionalities, CTF-PO71, as a sulfur cathode host, simultaneously demonstrates physical confinement and chemical anchoring of sulfur species, thus leading to superior capacity, cycling stability, and rate capability in comparison to unfunctionalized CTF. Meanwhile, as an adsorbent of organic dye molecules, CTF-PO71 was demonstrated to exhibit strong chemical interactions with dye molecules, facilitating adsorption kinetics and thereby promoting the adsorption rate and capacity. Furthermore, the dynamic adsorption experiments of organic dyes from solutions showed selectivity/priority of CTF-PO71s for specific dye molecules. Adsorption ; Triazine ; Amide ; Monomer ; Chemistry ; Chemical stability ; Chemical engineering ; Molecule ; Covalent bond ; Polymer chemistry ; Porosity ; Selectivity ; Catalysis ; Organic chemistry ; Polymer Covalent Organic Framework Applications ; Advanced Battery Materials and Technologies ; Advanced Photocatalysis Techniques
Authors: Ruimeng Zhang | Zebin Su | Xiaoyun Yan | Jiahao Huang | Wenpeng Shan | Xue‐Hui Dong | Xueyan Feng | Zhiwei Lin | Stephen Z. D. Cheng
Source: Chemistry - A European Journal (review, 2020-01-21)
Field: Materials Science — Biomaterials

Discovery of Structural Complexity through Self‐Assembly of Molecules Containing Rodlike Components Hierarchical structures are important for transferring and amplifying molecular functions to macroscopic properties of materials. In this regard, rodlike molecules have emerged as one of the most promising molecular building blocks to construct functional materials. Although the self-assembly of conventional molecules containing rodlike components generally results in nematic or layered smectic phases, due to the preferred parallel arrangements of rodlike components, extensive efforts have revealed that rational molecular design provides a versatile platform to engineer rich self-assembled structures. Herein, first successes achieved in polyphilic liquid crystals and rod-coil block systems are summarized. Special attention is paid to recent progress in the conjugation of rodlike building blocks with other molecular building blocks through the molecular Lego approach. Rod-based giant surfactants, sphere-rod conjugates, and dendritic rodlike molecules are covered. Future perspectives of the self-assembly of molecules containing rodlike components are also provided. Molecule ; Self-assembly ; Nanotechnology ; Liquid crystal ; Materials science ; Rational design ; Molecular engineering ; Chemical physics ; Block (permutation group theory) ; Chemistry ; Organic chemistry ; Optoelectronics Supramolecular Self-Assembly in Materials ; Surfactants and Colloidal Systems ; Luminescence and Fluorescent Materials
Authors: Philippe Schwaller | Alain C. Vaucher | Teodoro Laino | Jean‐Louis Reymond
Source: ChemRxiv (preprint, 2020-11-26)
Field: Materials Science — Materials Chemistry

Data augmentation strategies to improve reaction yield predictions and estimate uncertainty Chemical reactions describe how precursor molecules react together and transform into products. The reaction yield describes the percentage of the precursors successfully transformed into products relative to the theoretical maximum. The prediction of reaction yields can help chemists navigate reaction space and accelerate the design of more effective routes. Here, we investigate the best-studied high-throughput experiment data set and show how data augmentation on chemical reactions can improve yield predictions' accuracy, even when only small data sets are available. Previous work used molecular fingerprints, physics-based or categorical descriptors of the precursors. In this manuscript, we fine-tune natural language processing-inspired reaction transformer models on different augmented data sets to predict yields solely using a text-based representation of chemical reactions. When the random training sets contain 2.5% or more of the data, our models outperform previous models, including those using physics-based descriptors as inputs. Moreover, we demonstrate the use of test-time augmentation to generate uncertainty estimates, which correlate with the prediction errors. Categorical variable ; Chemical space ; Computer science ; Yield (engineering) ; Set (abstract data type) ; Data set ; Representation (politics) ; Experimental data ; Algorithm ; Artificial intelligence ; Machine learning ; Mathematics ; Chemistry ; Materials science ; Statistics ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Text Analysis Techniques
Authors: T. Aoyagi | Shinji Kohara | T. Naito | Yohei Onodera | Motomune Kodama | Taigo Onodera | Daiko Takamatsu | Shuta Tahara | Osami Sakata | Tatsuya Miyake | Kentaro Suzuya | Koji Ohara | Takeshi Usuki | Yamato Hayashi | Hirotsugu Takizawa
Source: Scientific Reports (article, 2020-04-28)
Field: Materials Science — Ceramics and Composites

Controlling oxygen coordination and valence of network forming cations Understanding the structure-property relationship of glass material is still challenging due to a lack of periodicity in disordered materials. Here, we report the properties and atomic structure of vanadium phosphate glasses characterized by reverse Monte Carlo modelling based on neutron/synchrotron X-ray diffraction and EXAFS data, supplemented by Raman and NMR spectroscopy. In vanadium-rich glass, the water durability, thermal stability and hardness improve as the amount of P<sub>2</sub>O<sub>5</sub> increases, and the network former of the glass changes from VO<sub>x</sub> polyhedra to the interplay between VO<sub>x</sub> polyhedra and PO<sub>4</sub> tetrahedra. We find for the first time that the coordination number of oxygen atoms around a V<sup>4+</sup> is four, which is an unusually small coordination number, and plays an important role for water durability, thermal stability and hardness. Furthermore, we show that the similarity between glass and crystal beyond the nearest neighbour distance is important for glass properties. These results demonstrate that controlling the oxygen coordination and valence of the network-forming cation is necessary for designing the properties of glass. Valence (chemistry) ; Coordination number ; Reverse Monte Carlo ; Vanadium ; Raman spectroscopy ; Materials science ; Extended X-ray absorption fine structure ; Crystallography ; Phosphate glass ; Thermal stability ; Crystal structure ; Neutron diffraction ; Synchrotron ; Oxygen ; Chemical physics ; Absorption spectroscopy ; Chemistry ; Ion ; Metallurgy ; Physics ; Optics Glass properties and applications ; Catalysis and Oxidation Reactions ; Luminescence Properties of Advanced Materials
Authors: Pao Li | Xinxin Zhang | Shangke Li | Guorong Du | Liwen Jiang | Xia Liu | Shenghua Ding | Yang Shan
Source: Sensors (article, 2020-03-12)
Field: Chemistry — Analytical Chemistry

A Rapid and Nondestructive Approach for the Classification of Different-Age Citri Reticulatae Pericarpium Using Portable Near Infrared Spectroscopy Citri Reticulatae Pericarpium (CRP), has been used in China for hundreds of years as a functional food and medicine. However, some short-age CRPs are disguised as long-age CRPs by unscrupulous businessmen in order to obtain higher profits. In this paper, a rapid and nondestructive method for the classification of different-age CRPs was established using portable near infrared spectroscopy (NIRS) in diffuse reflectance mode combination with appropriate chemometric methods. The spectra of outer skin and inner capsule of CRPs at different storage ages were obtained directly without destroying the samples. Principal component analysis (PCA) with single and combined spectral pretreatment methods was used for the classification of different-age CRPs. Furthermore, the data were pretreated with the PCA method, and Fisher linear discriminant analysis (FLD) with optimized pretreatment methods was discussed for improving the accuracy of classification. Data pretreatment methods can be used to eliminate the noise and background interference. The classification accuracy of inner capsule is better than that of outer skin data. Furthermore, the best results with 100% prediction accuracy can be obtained with FLD method, even without pretreatment. Linear discriminant analysis ; Principal component analysis ; Near-infrared spectroscopy ; Chemometrics ; Pattern recognition (psychology) ; Artificial intelligence ; Spectroscopy ; Computer science ; Mathematics ; Machine learning ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Essential Oils and Antimicrobial Activity
Authors: Emilio Díaz-Bejarano | Francesco Coletti | Sandro Macchietto
Source: Industrial & Engineering Chemistry Research (article, 2020-02-10)
Field: Materials Science — Biomaterials

A Model-Based Method for Visualization, Monitoring, and Diagnosis of Fouling in Heat Exchangers A critical review of current methods for monitoring the performance of heat exchangers in the presence of fouling highlights a number of pitfalls. An improved analysis method and visualization of operation data (the TH-λ plot) are proposed, which enable to accurately and rapidly estimate the location and extent of fouling, the properties of the deposit, and their impact on exchanger performance. The method uses advanced dynamic thermo-hydraulic models to analyze the data. The visualization presents this information in a way easily interpreted by field engineers. The superior features are demonstrated on various applications, where traditional methods give poor visibility or outright wrong information about underlying events. These include organic fouling deposition and aging, incomplete cleaning, multicomponent deposits, and changes in fouling behavior. First, the basic concepts are illustrated with idealized examples (constant inlet conditions, using simulated data). The approach is then applied to three real refining case studies, with pressure drop either measured or generated via soft sensors. The results show that the advanced dynamic models used enable to properly integrate and interpret highly variable data measurements, explain complex underlying thermal and hydraulic effects, adequately monitor performance, and rapidly detect changes in fouling behavior. The approach provides a new practical tool for monitoring of heat exchanger performance and early fouling diagnosis. Fouling ; Heat exchanger ; Computer science ; Visualization ; Process engineering ; Pressure drop ; Visibility ; Artificial intelligence ; Mechanical engineering ; Engineering ; Meteorology ; Chemistry ; Mechanics Calcium Carbonate Crystallization and Inhibition ; Heat Transfer and Optimization ; Advanced Thermodynamics and Statistical Mechanics
Authors: Richard Couperthwaite | Abhilash Molkeri | Danial Khatamsaz | Ankit Srivastava | Douglas Allaire | Raymundo Arróyave
Source: JOM (article, 2020-10-13)
Field: Materials Science — Materials Chemistry

Materials Design Through Batch Bayesian Optimization with Multisource Information Fusion Computer science ; Bayesian optimization ; Task (project management) ; Throughput ; Resource (disambiguation) ; Reduction (mathematics) ; Bayesian probability ; Industrial engineering ; Systems engineering ; Machine learning ; Artificial intelligence ; Engineering ; Mathematics Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Probabilistic and Robust Engineering Design
Authors: Issa I. Salame | Pauline Casino | Natasha Hodges
Source: IJCER (International Journal of Chemistry Education Research) (article, 2020-05-22)
Field: Chemistry — Physical and Theoretical Chemistry

Examining Challenges that Students Face in Learning Organic Chemistry Synthesis Organic chemistry is the offered after general chemistry and is the course that many find it challenging and difficult. Synthesis is first introduced in organic chemistry I course and is widely considered as one of the topics in which students struggle with and is evident in their performance in the topic. Our method of data collection is a Likert-type and open-ended questionnaire that was distributed at the end organic chemistry I course in an anonymous fashion. The collected data enabled us to examine the challenges students face in learning organic chemistry synthesis. Our findings support the notion that students have many difficulties with multistep organic chemistry synthesis including challenges recalling all of the varied required reactions, the amount of content and topics covered in organic chemistry, conceptual understanding of needed important topics such as mechanisms, acids and bases, nucleophiles and electrophiles, and stereochemistry, and problem-solving competency. Students view organic chemistry synthesis as challenging because of their reliance on memorization of a large number of reactions, reagents, and rules, poor conceptual understanding of the topics, ineffective teaching methods which lacks active learning and student engagement, and the myriad number of possible pathways to solve synthesis problems. Our participants suggest that memorization and rote-learning plays an important role in the learning of multistep organic synthesis, which might cause a hindrance to process of learning and can impede students’ problem-solving ability. Memorization ; Rote learning ; Chemistry ; Organic synthesis ; Chemistry education ; Mathematics education ; Concept learning ; Organic chemistry ; Psychology ; Teaching method ; Cooperative learning Various Chemistry Research Topics ; Innovative Teaching Methods ; Problem and Project Based Learning
Authors: Abbas Abdullah Baiz | Hamed Ahmadi | F. Shariatmadari | Mohammad Amir Karimi Torshizi
Source: Poultry Science (article, 2020-08-18)
Field: Chemistry — Analytical Chemistry

A Gaussian process regression model to predict energy contents of corn for poultry The present study proposes a Gaussian process regression (GPR) approach to develop a model to predict true metabolizable energy corrected for nitrogen (TMEn) content of corn samples (as model output) for poultry given levels of feed chemical compositions of crude protein, ether extract, crude fiber, and ash (as model inputs). A 30 corn samples obtained from 5 origins [Brazil (n = 9), China (n = 5), Iran (n = 7), and Ukraine (n = 9)] were assayed to determine chemical composition and TMEn content using chemical analyses and bioassay technique. In addition to GPR model, data were also analyzed by multiple linear regression (MLR) model. Results revealed that corn samples of different origins differ in their gross energy and chemical composition of crude protein, crude fiber, and ash, but no differences were observed for their ether extract and TMEn contents. Based on model evaluation criteria of R<sup>2</sup> and root mean square error (RMSE), the GPR model showed satisfactory performance (R<sup>2</sup> = 0.92 and RMSE = 33.68 kcal/kg DM) in predicting TMEn and produced relatively better prediction values than those produce by MLR (R<sup>2</sup> = 0.23 and RMSE = 104.85 kcal/kg DM). The GPR model may be capable of improving our aptitude and capacity to precisely predict energy contents of feed ingredients to formulate optimal diets for poultry. Linear regression ; Mean squared error ; Kriging ; Regression analysis ; Chemistry ; Chemical composition ; Mathematics ; Animal science ; Statistics ; Biology Spectroscopy and Chemometric Analyses ; Agriculture Sustainability and Environmental Impact ; Gaussian Processes and Bayesian Inference
Authors: Lishil Silvester | Aishah Naim | Alexandra Fateeva | G. Postole | Aline Auroux | Laurence Massin | P. Gélin | Laurence Bois
Source: Microporous and Mesoporous Materials (article, 2020-07-03)
Field: Chemistry — Inorganic Chemistry

Fine tuning of the physico-chemical properties of a MIL-53(Al) type - Mesoporous alumina composite using a facile sacrificial-template synthesis approach Mesoporous material ; Materials science ; Catalysis ; Composite number ; Porosity ; Chemical engineering ; Adsorption ; Template method pattern ; Composite material ; Nanotechnology ; Chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; MXene and MAX Phase Materials ; Advanced ceramic materials synthesis
Authors: Csaba Németh | Benjámin Gyarmati | Jenő Gacs | Diana V. Salakhieva | Kolos Molnár | Timur I. Abdullin | Krisztina László | András Szilágyi
Source: European Polymer Journal (article, 2020-03-17)
Field: Materials Science — Biomaterials

Fast dissolving nanofibrous matrices prepared by electrospinning of polyaspartamides Owing to their versatile chemistry, polyaspartamides have recently attracted increased interest in various biomedical uses such as drug delivery systems and scaffolding materials. Solubility of these polymers in organic solvents and in water at certain values of pH can be fine-tuned by their chemical composition, which was exploited here to fabricate fast-dissolving electrospun matrices in ethanol with no additives. Various side groups were tested to control the solubility of the polymers as well as the morphology and moisture uptake of the matrices produced. Tertiary amine groups were immobilized to ensure high solubility around neutral pH, while modification with alkyl side groups limited moisture uptake. Finally, 3-(diethylamino)propyl and n-butyl side groups were used in equal amounts. The effect of viscosity, surface tension and specific conductivity of polymer solutions on the fiber morphology was determined in order to optimize the conditions for preparing fibers with a narrow size distribution and large specific surface area. The polymer withstood the electrospinning process without any chemical degradation, as determined by IR, and was thermally stable. Furthermore, the matrices exhibited a glass transition temperature above room temperature. The complete release of vitamin B12 was observed within one minute due to the fast dissolution of the matrices in simulated salivary fluid at pH = 6.8, supporting the potential of the developed materials in oral drug delivery. Solubility ; Electrospinning ; Chemical engineering ; Polymer ; Dissolution ; Materials science ; Alkyl ; Drug delivery ; Surface tension ; Polymer chemistry ; Chemistry ; Organic chemistry ; Composite material ; Nanotechnology Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Conducting polymers and applications