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Matrix of orthogonalized atomic orbital coefficients representation for radicals and ions

Abstract (Expand)

Chemical (molecular, quantum) machine learning relies on representing molecules in unique and informative ways. Here, we present the matrix of orthogonalized atomic orbital coefficients (MAOC) as a … quantum-inspired molecular and atomic representation containing both structural (composition and geometry) and electronic (charge and spin multiplicity) information. MAOC is based on a cost-effective localization scheme that represents localized orbitals via a predefined set of atomic orbitals. The latter can be constructed from such small atom-centered basis sets as pcseg-0 and STO-3G in conjunction with guess (non-optimized) electronic configuration of the molecule. Importantly, MAOC is suitable for representing monatomic, molecular, and periodic systems and can distinguish compounds with identical compositions and geometries but distinct charges and spin multiplicities. Using principal component analysis, we constructed a more compact but equally powerful version of MAOC—PCX-MAOC. To test the performance of full and reduced MAOC and several other representations (CM, SOAP, SLATM, and SPAHM), we used a kernel ridge regression machine learning model to predict frontier molecular orbital energy levels and ground state single-point energies for chemically diverse neutral and charged, closed- and open-shell molecules from an extended QM7b dataset, as well as two new datasets, N-HPC-1 (N-heteropolycycles) and REDOX (nitroxyl and phenoxyl radicals, carbonyl, and cyano compounds). MAOC affords accuracy that is either similar or superior to other representations for a range of chemical properties and systems.

Authors: Stiv Llenga, Ganna Gryn’ova

Date Published: 7th Jun 2023

Publication Type: Journal

PfCRT mutations conferring piperaquine resistance in falciparum malaria shape the kinetics of quinoline drug binding and transport

Abstract (Expand)

The chloroquine resistance transporter (PfCRT) confers resistance to a wide range of quinoline and quinoline-like antimalarial drugs in Plasmodium falciparum , with local drug histories driving its …rum , with local drug histories driving its evolution and, hence, the drug transport specificities. For example, the change in prescription practice from chloroquine (CQ) to piperaquine (PPQ) in Southeast Asia has resulted in PfCRT variants that carry an additional mutation, leading to PPQ resistance and, concomitantly, to CQ re-sensitization. How this additional amino acid substitution guides such opposing changes in drug susceptibility is largely unclear. Here, we show by detailed kinetic analyses that both the CQ- and the PPQ-resistance conferring PfCRT variants can bind and transport both drugs. Surprisingly, the kinetic profiles revealed subtle yet significant differences, defining a threshold for in vivo CQ and PPQ resistance. Competition kinetics, together with docking and molecular dynamics simulations, show that the PfCRT variant from the Southeast Asian P . falciparum strain Dd2 can accept simultaneously both CQ and PPQ at distinct but allosterically interacting sites. Furthermore, combining existing mutations associated with PPQ resistance created a PfCRT isoform with unprecedented non-Michaelis-Menten kinetics and superior transport efficiency for both CQ and PPQ. Our study provides additional insights into the organization of the substrate binding cavity of PfCRT and, in addition, reveals perspectives for PfCRT variants with equal transport efficiencies for both PPQ and CQ.

Authors: Guillermo M. Gomez, Giulia D’Arrigo, Cecilia P. Sanchez, Fiona Berger, Rebecca C. Wade, Michael Lanzer

Date Published: 7th Jun 2023

Publication Type: Journal

Formation of star clusters and enrichment by massive stars in simulations of low-metallicity galaxies with a fully sampled initial stellar mass function

Abstract

Not specified

Authors: Natalia Lahén, Thorsten Naab, Guinevere Kauffmann, Dorottya Szécsi, Jessica May Hislop, Antti Rantala, Alexandra Kozyreva, Stefanie Walch, Chia-Yu Hu

Date Published: 1st Jun 2023

Publication Type: Journal

The contribution of binary star formation via core fragmentation on protostellar multiplicity

Abstract (Expand)

Context. Observations of young multiple star systems find a bimodal distribution in companion frequency and separation. The origin of these peaks has often been attributed to binary formation via core …been attributed to binary formation via core and disc fragmentation. However, theory and simulations suggest that young stellar systems that form via core fragmentation undergo significant orbital evolution. Aims. We investigate the influence of the environment on the formation and orbital evolution of multiple star systems, and how core fragmentation contributes to the formation of close (20 − 100 AU) binaries. We use multiple simulations of star formation in giant molecular clouds and compare them to the multiplicity statistics of the Perseus star-forming region. Methods. Simulations were run with the adaptive mesh refinement code RAMSES with sufficient resolution to resolve core fragmentation beyond 400 AU and dynamical evolution down to 16.6 AU, but without the possibility of resolving disc fragmentation. The evolution of the resulting stellar systems was followed over millions of years. Results. We find that star formation in lower gas density environments is more clustered; however, despite this, the fractions of systems that form via dynamical capture and core fragmentation are broadly consistent at ∼40% and ∼60%, respectively. In all gas density environments, we find that the typical scale at which systems form via core fragmentation is 10 3 − 3.5  AU. After formation, we find that systems that form via core fragmentation have slightly lower inspiral rates (∼10 −1.68  AU yr −1 measured over the first 10 000 yr) compared to dynamical capture (∼10 −1.32  AU yr −1 ). We then compared the simulation with the conditions most similar to the Perseus star-forming region to determine whether the observed bimodal distribution can be replicated. We find that it can be replicated, but it is sensitive to the evolutionary state of the simulation. Conclusions. Our results indicate that a significant number of low-mass close binaries with separations from 20 − 100 AU can be produced via core fragmentation or dynamical capture due to efficient inspiral, without the need for a further contribution from disc fragmentation.

Authors: Rajika L. Kuruwita, Troels Haugbølle

Date Published: 1st Jun 2023

Publication Type: Journal

From classification accuracy to proper scoring rules: Elicitability of probabilistic top list predictions

Abstract

Not specified

Author: Johannes Resin

Date Published: 31st May 2023

Publication Type: Journal

Engineering Host–Guest Interactions in Organic Framework Materials for Drug Delivery

Abstract (Expand)

Metal-organic frameworks (MOF) and covalent organic frameworks (COFs) are promising nanocarriers for targeted drug delivery. Noncovalent interactions between frameworks and drugs play a fundamental … role in the therapeutic uptake and release of the latter. However, the scope of framework functionalizations and deliverable drugs remains underexplored. Using a multilevel approach combining molecular docking and density functional theory, we show for a range of drugs and frameworks that experimentally reported release metrics are in good agreement with the in silico computed host–guest interaction energies. Functional groups within the framework significantly impact the strength of these host–guest interactions, while a given framework can serve as an efficient delivery agent for drugs beyond the prototypical few. Our findings identify the interaction energy as a reliable and relatively easy to compute descriptor of organic framework materials for drug delivery, able to facilitate their high-throughput screening and targeted design towards extended-release times.

Authors: Michelle Ernst, Ganna Gryn'ova

Date Published: 26th May 2023

Publication Type: Journal

Statistical postprocessing of numerical weather prediction forecasts using machine learning

Abstract

Not specified

Author: Benedikt Schulz

Date Published: 25th May 2023

Publication Type: Doctoral Thesis

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