jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alessandro Tibo | Jiazhen He | Jon Paul Janet | Eva Nittinger | Ola Engkvist
Source: ChemRxiv (preprint, 2024-05-21)
Field: Chemistry — Physical and Theoretical Chemistry

Exhaustive local chemical space exploration usinga transformer model How many near-neighbors does a molecule have? This is a simple, fundamental, but unsolved question in chemistry. It is key for solving many important molecular optimization problems, for example in lead optimization in drug discovery under the similarity principle assumption. Generative models can sample virtual molecules from a vast theoretical chemical space, but so far have lacked explicit knowledge about molecular similarity. This means that a generative model needs to be guided by reinforcement learning or another learning mechanism to be able to sample a relevant similar chemical space. Correspondingly the generative model provide no mechanism for quantifying how completely it can sample a particular region of the chemical space. To overcome these limitations, a novel source-target molecular transformer model is proposed, regularized via a similarity kernel function. It has been trained on, to the best of our knowledge, the largest data set of molecular pairs so far consisting of ≥ billion pairs. The regularization term enforces a direct relationship between the probability of generating a target molecule and its similarity to a given source molecule. The model is able to systematically sample compounds ordered by their probability and accordingly by their similarity. In combination with a deterministic sampling strategy, beam search, it is possible for the first time to comprehensively explore the near-neighborhood around a specific compound. Our results show that the regularization term helps to substantially improve the correlation between the probability of generating a target molecule and its similarity to the source molecule. The trained transformer model is able to exhaustively sample a near-neighborhood around a given drug-like molecule. Chemical space ; Transformer ; Space (punctuation) ; Computer science ; Engineering ; Chemistry ; Electrical engineering ; Voltage Various Chemistry Research Topics
Authors: Donald Bistri | Ignacio Arretche | Jacob J. Lessard | Michael Zakoworotny | Sagar Vyas | Laurence Rongy | Rafael Gómez Bombarelli | Jeffrey S. Moore | Philippe H. Geubelle
Source: ChemRxiv (preprint, 2024-05-15)
Field: Chemistry — Organic Chemistry

A Mechanism-Based Reaction-Diffusion Model for Accelerated Discovery of Thermoset Resins Frontally Polymerized by Olefin Metathesis Frontal ring-opening metathesis polymerization (FROMP) involves a self-perpetuating exothermic reaction, which enables the rapid and energy-efficient manufacturing of thermoset polymers and composites. Current state-of-the-art reaction-diffusion FROMP models rely on a phenomenological description of the olefin metathesis kinetics, limiting their ability to model the governing thermo-chemical FROMP processes. Furthermore, the existing models are unable to predict the variations in FROMP kinetics with changes in the resin composition and as a result are of limited utility towards accelerated discovery of new resin formulations. In this manuscript, we formulate a chemically meaningful model grounded in the established mechanism of ring-opening metathesis polymerization (ROMP). Our study aims to validate the hypothesis that the ROMP mechanism, applicable to monomer-initiator solutions below 100°C, remains valid under the non-ideal conditions encountered in FROMP, including ambient to >200°C temperatures, sharp temperature gradients, and neat monomer environments. Through extensive simulations, we demonstrate that our mechanism-based model accurately predicts FROMP behavior across various resin compositions, including polymerization front velocities and thermal characteristics (e.g., Tmax). Additionally, we introduce a semi-inverse workflow that predicts FROMP behavior from a single experimental data point. Notably, the physiochemical parameters utilized in our model can be obtained through DFT calculations and minimal experiments, highlighting the model's potential for rapid screening of new FROMP chemistries in pursuit of thermoset polymers with superior thermo-chemo-mechanical properties. Thermosetting polymer ; Olefin metathesis ; Mechanism (biology) ; Diffusion ; Olefin fiber ; Polymerization ; Chemistry ; Materials science ; Metathesis ; Organic chemistry ; Physics ; Catalysis ; Thermodynamics ; Polymer Synthetic Organic Chemistry Methods ; Organometallic Complex Synthesis and Catalysis ; Carbon dioxide utilization in catalysis
Authors: Masami Sako | Nobuaki Yasuo | Masakazu Sekijima
Source: ChemRxiv (preprint, 2024-07-30)
Field: Chemistry — Spectroscopy

DiffInt: A Pharmacophore-Aware Diffusion Model for Structure-Based Drug Design with Explicit Hydrogen Bond Interaction Guidance The design of drug molecules is a critical stage in the drug discovery process. The use of pharmacophore models in structure-based drug discovery has long played an important role in efficient development. Significant progress has been made in recent years in the generation of 3D molecules via deep generation models. However, while many existing models have succeeded in incorporating structural information of target proteins, they have not been able to address important interactions between proteins and drug molecules, especially hydrogen bonds, explicitly. In this study, we propose DiffInt as a novel structure-based approach that explicitly addresses interactions. The model naturally incorporates hydrogen bonds between the protein and ligand by treating them as pseudoparticles. The experimental results show that DiffInt reproduces hydrogen bonds and that the hydrogen binding energies significantly outperform those of existing models. To facilitate the use of our tool for generating new drug molecules based on any protein three-dimensional structure, we have made the source code and trained model available on GitHub (https://github.com/sekijima-lab/DiffInt) under the MIT license, with the execution environment provided on Google Colab. Pharmacophore ; Hydrogen bond ; Drug ; Diffusion ; Computer science ; Chemistry ; Combinatorial chemistry ; Stereochemistry ; Pharmacology ; Medicine ; Organic chemistry ; Physics ; Molecule ; Thermodynamics Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Chemical Reaction Mechanisms
Authors: Siyeop Yoon | Jay Sairam Pratap | Wanjia Liu | Matthew Tivnan | Hui Ren | Abhiram R. Bhashyam | Quanzheng Li | Neal C. Chen | Xiang Li
Source:  (article, 2024-05-27)
Field: Materials Science — Surfaces, Coatings and Films

High-Resolution 3d Ct Synthesis From Bidirectional X-Ray Images Using 3d Diffusion Model 3D Computed Tomography (CT) offers invaluable geometric insights into bone structures, but the high radiation dose and medical cost constraints are significant barriers. Moreover, CT reconstruction demands multiple X-ray projections, necessitating a dedicated scanning system, whereas bidirectional X-rays are already the front-line diagnostic tool in routine practice. Therefore, reconstructing 3D bone structures from bidirectional X-ray data can reduce the need for additional CT scans, provide rapid access to 3D information, and lower medical costs. Recently, diffusion models have emerged as potent tools for generating high-fidelity images. However, high computational costs have limited their utility. Collecting large-scale datasets for training in clinical environments presents another challenge.In this study, we introduce a novel approach to synthesize 3D CT volumes from a bidirectional X-ray projection using a 3D diffusion model. To reduce the computational burden and the need for a large dataset, our 3D diffusion model was trained using patch-wise loss. A conditional score function of our model incorporates 2D bidirectional X-ray images and patch coordinate information to synthesize high-resolution CT. Initial findings indicate that our diffusion model synthesizes 3D CT volumes from a bidirectional X-ray, effectively capturing 3D geometric correlations while enabling single-GPU training and rapid 3D volumetric sampling. Computer science ; Diffusion ; Resolution (logic) ; Image resolution ; Computer vision ; Artificial intelligence ; Computer graphics (images) ; Physics Electron and X-Ray Spectroscopy Techniques ; Advancements in Photolithography Techniques ; Image Processing Techniques and Applications
Authors: Ziyue Zhong | Angela Ren | Lance Arbilo | Qiangnan Zhang | William C. Smith | Bin Qin | Yan Wang | Feng Zhang
Source: Journal of Pharmaceutical Sciences (article, 2024-11-12)
Field: Materials Science — Polymers and Plastics

Correlation between polymer properties, extrusion process parameters and permeability of etonogestrel in ethylene vinyl acetate films Ethylene-vinyl acetate ; Copolymer ; Extrusion ; Materials science ; Vinyl acetate ; Ethylene ; Permeability (electromagnetism) ; Chemical engineering ; Polymer chemistry ; Composite material ; Polymer ; Chemistry ; Organic chemistry ; Catalysis Synthesis and properties of polymers ; Polymer Science and PVC ; Advanced Polymer Synthesis and Characterization
Authors: Yule Wu | Jiamei Wang | Xianglong Guo | Lefu Zhang
Source: Corrosion Science (article, 2024-10-04)
Field: Materials Science — Metals and Alloys

Conditional generative adversarial network-based predictive method for crack initiation in a dual-phase austenite stainless weld Austenite ; Welding ; Generative adversarial network ; Materials science ; Corrosion ; Dual (grammatical number) ; Phase (matter) ; Generative grammar ; Metallurgy ; Adversarial system ; Computer science ; Artificial intelligence ; Microstructure ; Deep learning ; Physics Hydrogen embrittlement and corrosion behaviors in metals ; Welding Techniques and Residual Stresses ; Microstructure and Mechanical Properties of Steels
Authors: Jiahan Li | Chaoran Cheng | Zuofan Wu | Ruihan Guo | Shitong Luo | Zhizhou Ren | Jian Peng | Jianzhu Ma
Source: arXiv (Cornell University) (preprint, 2024-06-02)
Field: Chemistry — Spectroscopy

Full-Atom Peptide Design based on Multi-modal Flow Matching Peptides, short chains of amino acid residues, play a vital role in numerous biological processes by interacting with other target molecules, offering substantial potential in drug discovery. In this work, we present PepFlow, the first multi-modal deep generative model grounded in the flow-matching framework for the design of full-atom peptides that target specific protein receptors. Drawing inspiration from the crucial roles of residue backbone orientations and side-chain dynamics in protein-peptide interactions, we characterize the peptide structure using rigid backbone frames within the $\mathrm{SE}(3)$ manifold and side-chain angles on high-dimensional tori. Furthermore, we represent discrete residue types in the peptide sequence as categorical distributions on the probability simplex. By learning the joint distributions of each modality using derived flows and vector fields on corresponding manifolds, our method excels in the fine-grained design of full-atom peptides. Harnessing the multi-modal paradigm, our approach adeptly tackles various tasks such as fix-backbone sequence design and side-chain packing through partial sampling. Through meticulously crafted experiments, we demonstrate that PepFlow exhibits superior performance in comprehensive benchmarks, highlighting its significant potential in computational peptide design and analysis. Modal ; Matching (statistics) ; Flow (mathematics) ; Peptide ; Computer science ; Chemistry ; Mathematics ; Physics ; Mechanics ; Statistics ; Biochemistry ; Polymer chemistry Mass Spectrometry Techniques and Applications ; Machine Learning in Materials Science ; Chemical Synthesis and Analysis
Authors: Benjamin Kaufman | Edward Williams | Ryan Pederson | Carl Underkoffler | Zahid Panjwani | Miles Wang-Henderson | Narbe Mardirossian | Matthew H. Katcher | Zack Strater | Jean-Marc Grandjean | B.H. Lee | John Parkhill
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-08-22)
Field: Chemistry — Spectroscopy

Latent Diffusion for Conditional Generation of Molecules A bstract Designing a small molecule therapeutic is a challenging multi-parameter optimization problem. Key properties, such as potency, selectivity, bioavailability, and safety must be jointly optimized to deliver an effective clinical candidate. We present COATI-LDM, a novel application of latent diffusion models to the conditional generation of property-optimized, drug-like small molecules. Diffusive generation of latent molecular encodings, rather than direct diffusive generation of molecular structures, offers an appealing way to handle the small and mismatched datasets that are common for molecular properties. We benchmark various diffusion guidance schemes and sampling methods against a pre-trained autoregressive transformer and genetic algorithms to evaluate control over potency, expert preference, and various physicochemical properties. We show that conditional diffusion allows control over the properties of generated molecules, with practical and performance advantages over competing methods. We also apply the recently introduced idea of particle guidance to enhance sample diversity. We prospectively survey a panel of medicinal chemists and determine that we can conditionally generate molecules that align with their preferences via a learned preference score. Finally, we present a partial diffusion method for the local optimization of molecular properties starting from a seed molecule. Conditional generation of small molecules using latent diffusion models on molecular encodings provides a highly practical and flexible alternative to prior molecular generation schemes. Diffusion ; Computer science ; Statistical physics ; Physics ; Thermodynamics Analytical Chemistry and Chromatography
Authors: Pedram Tootoonchian | Gábor Holló | Rana Uzunlar | István Lagzi | Bilge Baytekin
Source: Langmuir (article, 2024-05-17)
Field: Chemistry — Physical and Theoretical Chemistry

Periodic Stratification of Colloids in a Liquid Phase Produced by a Precipitation Reaction and Gel Swelling Pattern formation is a frequent phenomenon occurring in animate and inanimate systems. The interplay between the mass transport of the chemical species and the underlying chemical reaction networks generates most patterns in chemical systems. Periodic precipitation is an emblematic example of reaction-diffusion patterns, in which the process generates a spatial periodic structure in porous media. Here, we use the dormant reagent method to produce colloidal particles of Prussian blue (PB) and PB analogues at the liquid-gel interface. The generated particles produced a stable periodic stratification pattern in time in the liquid phase placed on top of the solid hydrogel. The phenomenon is governed by periodic swelling of the gel driven by the osmotic stress and stability of the formed particles. To illustrate the phenomenon, we developed an extended reaction-diffusion model, which incorporated the gel swelling and sedimentation effect of the formed colloids and could qualitatively reproduce the pattern formation in the liquid phase. Swelling ; Colloid ; Stratification (seeds) ; Precipitation ; Chemical engineering ; Phase (matter) ; Chemistry ; Chromatography ; Materials science ; Composite material ; Organic chemistry ; Meteorology ; Physics Electrostatics and Colloid Interactions ; Diatoms and Algae Research ; Field-Flow Fractionation Techniques
Authors: Miko M. Stulajter | Dmitrij Rappoport
Source: ChemRxiv (preprint, 2024-05-31)
Field: Materials Science — Materials Chemistry

Reaction Networks Resemble Low-Dimensional Regular Lattices The computational exploration, manipulation, and design of complex chemical reactions face fundamental challenges related to the high-dimensional nature of potential energy surfaces (PESs) that govern reactivity. Accurately modeling complex reactions is crucial for understanding the chemical processes involved in, for example, organocatalysis, autocatalytic cycles, and one-pot molecular assembly. Our prior research demonstrated that discretizing PESs using heuristics based on bond breaking and bond formation produces a reaction network representation with a low-dimensional structure (metric space). We now find that these reaction networks possess additional, though approximate, structure and resemble low-dimensional regular lattices with a small amount of random edge rewiring. The heuristics-based discretization thus generates a nonlinear dimensionality reduction by a factor of ten with an a posteriori error measure (probability of random rewiring). The structure becomes evident through a comparative analysis of CHNO reaction networks of varying stoichiometries against a panel of size-matched generative network models, taking into account their local, metric, and global properties. The generative models include random networks (Erdős-Rényi and bipartite random networks), regular lattices (periodic and non-periodic), and network models with a tunable level of "randomness" (Watts-Strogatz graphs and regular lattices with random rewiring). The CHNO networks are simultaneously closely matched in all these properties by 3-4-dimensional regular lattices with 10% or less of edges randomly rewired. The effective dimensionality reduction is found to be independent of the system size, stoichiometry, and rule set, suggesting that search and sampling algorithms for PESs of complex chemical reactions can be effectively leveraged. Computer science Machine Learning in Materials Science ; Bioinformatics and Genomic Networks ; Gene Regulatory Network Analysis
Authors: Ping Bao | Yipeng Zhou | Xiaowei Wang | Jinfeng Men | Chengqiang Liang | Hao Ding
Source: Radiochimica Acta (article, 2024-07-24)
Field: Chemistry — Inorganic Chemistry

Study on the adsorption performance of zeolite imidazole frameworks materials for Co(II) and Mn(II) in solution Abstract The radionuclides 60 Co and 54 Mn are the main activation products produced in the operation of nuclear power facilities. Wastewater with these radionuclides must be treated to meet standards before being discharged. A variety of zeolite imidazole frameworks (ZIFs) materials were synthesized at room temperature, and the adsorption effect of ZIF-67 was found to be the best through adsorption experiments on Co(II) and Mn(II). The thermal stability test and structural characterization of ZIF-67 were carried out. At the same time, the influence of the initial pH value, adsorption time, and initial concentration of the solution on the adsorption of Co(II) and Mn(II) by ZIF-67 was investigated. The results show that: ZIF-67 has a microporous structure with a BET surface area of 1,035.72 m 2 /g. In addition, ZIF-67 has good thermal stability, under the condition of pH = 6, a temperature of 303 K and the initial concentration of 500 mg/L. The saturated adsorption capacity for Co(II) and Mn(II) reached 230.25 mg/g and 338.75 mg/g, respectively. ZIF-67 exhibits good selective adsorption performance for Co(II) and Mn (II) in high concentration interfering ion solutions and multi-ion solutions. The adsorption process of ZIF-67 was analyzed by kinetics, thermodynamics, isotherms, and adsorption diffusion models. The analysis of thermodynamic parameters shows that the adsorption process of ZIF-67 to Co(II) and Mn(II) is spontaneous and endothermic. The pseudo-second-order kinetic model, Langmuir isotherm model, and Boyd model are more in line with the adsorption process of Co(II) and Mn(II) by ZIF-67. It shows that the active sites on the surface of ZIF-67 are evenly distributed, and the adsorption process is single-molecule chemical layer adsorption. In addition, the liquid film diffusion dominates the adsorption rate during the adsorption process of Co(II) and Mn(II) by ZIF-67. Chemistry ; Adsorption ; Microporous material ; Endothermic process ; Zeolite ; Langmuir adsorption model ; Imidazole ; Thermal stability ; Inorganic chemistry ; Nuclear chemistry ; Physical chemistry ; Catalysis ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Adsorption and biosorption for pollutant removal
Authors: R. Nalini Suja | B. Sridevi | Dheeraj Kumar
Source: Journal of Research Updates in Polymer Science (article, 2024-09-02)
Field: Materials Science — Polymers and Plastics

Determination of Kinetic Parameters and Thermal Characteristics of Epoxy Casuarina Bio Composites By reinforcing casuarina equisetifolia leaf, a flexible bio fibre, in different volume proportions of 4%, 8%, 12%, 16%, and 20% into the epoxy resin, a bio-based composite was produced utilizing the hand layup method. FT IR spectrum was used to verify the production of epoxy casuarina composite. The produced composites have undergone an extensive thermal investigation using Differential Thermal Analysis (DTA), Thermogravimetric Analysis (TGA), Derivative Thermogravimetric (DTG) study and Differential Scanning Calorimetry (DSC) analysis. It has been observed that increasing the percentage of casuarina fibre content enhances the thermal stability of epoxy resin. Horowitz- Metzger (HM), Broido and Coats-Redfern (CR) models were worked and the result revealed that the decomposition of casuarina epoxy composite followed one Dimensional Diffusion Model with coefficient of correlation value close to 1. The thermodynamic parameters including Activation energy (Ea), frequency factor (A), Gibbs free energy (ΔG), entropy (ΔS) and enthalpy (ΔH) of epoxy casuarina composites were found to increase with increase in fibre contents. This makes the composite more suitable for making high performance engineering material for various applications. Materials science ; Epoxy ; Composite material ; Casuarina ; Kinetic energy ; Thermal ; Thermodynamics ; Botany Natural Fiber Reinforced Composites ; Polymer Nanocomposites and Properties ; Polymer crystallization and properties
Authors: Claudir Gabriel Kaufmann | Rúbia Young Sun Zampiva | Annelise Kopp Alves | RAFAEL CIDADE TRONCO | Marco Rossi | C. Bergman | Leonardo Vidal Zancanaro | Sérgio Roberto Mortari | Cristiano Rodrigo Bohn Rhoden
Source: Environmental Science and Pollution Research (article, 2024-12-11)
Field: Chemistry — Organic Chemistry

CNT@NiFe2O4 nanocomposite for phenobarbital removal: adsorption study and characterization Pollutant ; Phenobarbital ; Contamination ; Environmental remediation ; Environmental science ; Human health ; Ecotoxicology ; Effluent ; Environmental chemistry ; Groundwater ; Adsorption ; Chemistry ; Environmental engineering ; Environmental health ; Ecology ; Geology ; Medicine ; Organic chemistry ; Biology Nanomaterials for catalytic reactions ; Adsorption and biosorption for pollutant removal ; Graphene research and applications
Authors: Dalia Grigaliūnienė | Povilas Balčius | Diederik Jacques | Povilas Poškas
Source: Environmental Earth Sciences (article, 2024-09-28)
Field: Chemistry — Inorganic Chemistry

Assessment of intermediate level radioactive waste disposal cell evolution in granitic host rock applying abstracted reactive transport models Radioactive waste ; Biogeosciences ; Granitic rock ; Pluton ; Geology ; High-level waste ; Geochemistry ; Waste disposal ; Mining engineering ; Radionuclide ; Hydrology (agriculture) ; Environmental science ; Waste management ; Earth science ; Geotechnical engineering ; Nuclear physics ; Engineering Radioactive element chemistry and processing ; Graphite, nuclear technology, radiation studies ; Nuclear and radioactivity studies