Materials Discovery with Extreme Properties via Reinforcement Learning-Guided Combinatorial Chemistry

The goal of most materials discovery is to discover materials that are superior to those currently known. Fundamentally, this is close to extrapolation, which is a weak point for most machine learning models that learn the probability distribution of data. Herein, we develop reinforcement learning-guided combinatorial chemistry, which is a rule-based molecular designer driven by trained policy for selecting subsequent molecular fragments to get a target molecule. Since our model has the potential to generate all possible molecular structures that can be obtained from combinations of molecular fragments, unknown molecules with superior properties can be discovered. We theoretically and empirically demonstrate that our model is more suitable for discovering better compounds than probability distribution-learning models. In an experiment aimed at discovering molecules that hit seven extreme target properties, our model discovered 1,315 of all target-hitting molecules and 7,629 of five target-hitting molecules out of 100,000 trials, whereas the probability distribution-learning models failed. Moreover, it has been confirmed that every molecule generated under the binding rules of molecular fragments is 100% chemically valid. To illustrate the performance in actual problems, we also demonstrate that our models work well on two practical applications: discovering protein docking molecules and HIV inhibitors.Comment: 18 pages, 8 figure

Performance Comparison of Design Optimization and Deep Learning-based Inverse Design

Surrogate model-based optimization has been increasingly used in the field of engineering design. It involves creating a surrogate model with objective functions or constraints based on the data obtained from simulations or real-world experiments, and then finding the optimal solution from the model using numerical optimization methods. Recent advancements in deep learning-based inverse design methods have made it possible to generate real-time optimal solutions for engineering design problems, eliminating the requirement for iterative optimization processes. Nevertheless, no comprehensive study has yet closely examined the specific advantages and disadvantages of this novel approach compared to the traditional design optimization method. The objective of this paper is to compare the performance of traditional design optimization methods with deep learning-based inverse design methods by employing benchmark problems across various scenarios. Based on the findings of this study, we provide guidelines that can be taken into account for the future utilization of deep learning-based inverse design. It is anticipated that these guidelines will enhance the practical applicability of this approach to real engineering design problems

Minimal grid diagrams of the prime knots with crossing number 14 and arc index 13

There are 46,972 prime knots with crossing number 14. Among them 19,536 are alternating and have arc index 16. Among the non-alternating knots, 17, 477, and 3,180 have arc index 10, 11, and 12, respectively. The remaining 23,762 have arc index 13 or 14. There are none with arc index smaller than 10 or larger than 14. We used the Dowker-Thistlethwaite code of the 23,762 knots provided by the program Knotscape to locate non-alternating edges in their diagrams. Our method requires at least six non-alternating edges to find arc presentations with 13 arcs. We obtained 8,027 knots having arc index 13. We show them by their minimal grid diagrams. The remaining 15,735 prime non-alternating 14 crossing knots have arc index 14 as determined by the lower bound obtained from the Kauffman polynomial.11 pages, 8 figures, 200 grid diagrams. Interested readers may typeset for 8,027 grid diagrams following authors\u27 instruction. arXiv admin note: substantial text overlap with arXiv:2402.0271

DFT‐Guided Discovery of Ethynyl‐Triazolyl‐Phosphinates as Modular Electrophiles for Chemoselective Cysteine Bioconjugation and Profiling

We report the density functional theory (DFT) guided discovery of ethynyl‐triazolyl‐phosphinates (ETPs) as a new class of electrophilic warheads for cysteine selective bioconjugation. By using CuI‐catalysed azide alkyne cycloaddition (CuAAC) in aqueous buffer, we were able to access a variety of functional electrophilic building blocks, including proteins, from diethynyl‐phosphinate. ETP‐reagents were used to obtain fluorescent peptide‐conjugates for receptor labelling on live cells and a stable and a biologically active antibody‐drug‐conjugate. Moreover, we were able to incorporate ETP‐electrophiles into an azide‐containing ubiquitin under native conditions and demonstrate their potential in protein–protein conjugation. Finally, we showcase the excellent cysteine‐selectivity of this new class of electrophile in mass spectrometry based, proteome‐wide cysteine profiling, underscoring the applicability in homogeneous bioconjugation strategies to connect two complex biomolecules.By means of density functional theory calculations, ethynyl‐triazolyl‐phosphinates (ETPs) were discovered as modular and cysteine‐selective electrophiles for bioconjugation. Using CuI‐click chemistry in aqueous buffers, this functional group can be easily introduced into azide‐containing (bio‐)molecules. These reagents can be used for proteome‐wide cysteine profiling and to obtain functional peptide‐ and protein conjugates, as well as protein–protein conjugates . imageDeutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659Leibniz-Gemeinschaft http://dx.doi.org/10.13039/501100001664Studienstiftung des Deutschen Volkes http://dx.doi.org/10.13039/501100004350Alexander von Humboldt-Stiftung http://dx.doi.org/10.13039/100005156Institute for Basic Science in KoreaPeer Reviewe

DFT‐basierte Entdeckung von Ethynyl‐Triazolyl‐Phosphinaten als modulare Elektrophile für die chemoselektive Cystein‐Biokonjugation und Profilierung

Wir berichten über eine Dichtefunktionaltheorie (DFT)-basierte Entdeckung von Ethinyl-Triazolyl-Phosphinaten (ETP) als eine neue Klasse elektrophiler Verbindungen für die selektive Biokonjugation von Cystein. Mit Hilfe der CuI-katalysierten Azid-Alkin-Cycloaddition (CuAAC) in wässrigem Puffer konnten wir eine Vielzahl funktioneller elektrophiler Bausteine, darunter auch Proteine, aus Diethynylphosphinat herstellen. Wir verwendeten diese ETP-Reagenzien, um fluoreszierende Peptid-Konjugate für die Markierung von Rezeptoren auf lebenden Zellen sowie ein stabiles und biologisch aktives Antikörper-Wirkstoff-Konjugat zu erhalten. Darüber hinaus konnten wir ETP-Elektrophile unter nativen Bedingungen in ein Azid-haltiges Ubiquitin einbauen und ihr Potenzial für die Protein-Protein-Konjugation demonstrieren. Schließlich zeigen wir die exzellente Cystein-Selektivität dieser neuen Klasse von Elektrophilen in Massenspektrometrie basierten, proteomweiten Reaktivitätsstudien und unterstreichen damit die generelle Anwendbarkeit in homogenen Biokonjugationsstrategien zur Verknüpfung zweier komplexer Biomoleküle.Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659Leibniz-Gemeinschaft http://dx.doi.org/10.13039/501100001664Studienstiftung des Deutschen Volkes http://dx.doi.org/10.13039/501100004350Alexander von Humboldt-Stiftung http://dx.doi.org/10.13039/100005156Institute for Basic Science in KoreaPeer Reviewe

Increased viral load in patients infected with severe acute respiratory syndrome coronavirus 2 Omicron variant in the Republic of Korea

Objectives Coronavirus disease 2019 (COVID-19) has been declared a global pandemic owing to the rapid spread of the causative agent, severe acute respiratory syndrome coronavirus 2. Its Delta and Omicron variants are more transmissible and pathogenic than other variants. Some debates have emerged on the mechanism of variants of concern. In the COVID-19 wave that began in December 2021, the Omicron variant, first reported in South Africa, became identifiable in most cases globally. The aim of this study was to provide data to inform effective responses to the transmission of the Omicron variant. Methods The Delta variant and the spike protein D614G mutant were compared with the Omicron variant. Viral loads from 5 days after symptom onset were compared using epidemiological data collected at the time of diagnosis. Results The Omicron variant exhibited a higher viral load than other variants, resulting in greater transmissibility within 5 days of symptom onset. Conclusion Future research should focus on vaccine efficacy against the Omicron variant and compare trends in disease severity associated with its high viral load

Ultrahigh strength, modulus, and conductivity of graphitic fibers by macromolecular coalescence

Theoretical considerations suggest that the strength of carbon nanotube (CNT) fibers be exceptional; however, their mechanical performance values are much lower than the theoretical values. To achieve macroscopic fibers with ultrahigh performance, we developed a method to form multidimensional nanostructures by coalescence of individual nanotubes. The highly aligned wet-spun fibers of single- or double-walled nanotube bundles were graphitized to induce nanotube collapse and multi-inner walled structures. These advanced nanostructures formed a network of interconnected, close-packed graphitic domains. Their near-perfect alignment and high longitudinal crystallinity that increased the shear strength between CNTs while retaining notable flexibility. The resulting fibers have an exceptional combination of high tensile strength (6.57 GPa), modulus (629 GPa), thermal conductivity (482 W/m·K), and electrical conductivity (2.2 MS/m), thereby overcoming the limits associated with conventional synthetic fibers

HyperCLOVA X Technical Report

We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding. HyperCLOVA X was trained on a balanced mix of Korean, English, and code data, followed by instruction-tuning with high-quality human-annotated datasets while abiding by strict safety guidelines reflecting our commitment to responsible AI. The model is evaluated across various benchmarks, including comprehensive reasoning, knowledge, commonsense, factuality, coding, math, chatting, instruction-following, and harmlessness, in both Korean and English. HyperCLOVA X exhibits strong reasoning capabilities in Korean backed by a deep understanding of the language and cultural nuances. Further analysis of the inherent bilingual nature and its extension to multilingualism highlights the model's cross-lingual proficiency and strong generalization ability to untargeted languages, including machine translation between several language pairs and cross-lingual inference tasks. We believe that HyperCLOVA X can provide helpful guidance for regions or countries in developing their sovereign LLMs.Comment: 44 pages; updated authors list and fixed author name