We present the open-source framework kallisto that enables the {efficient|effective} and robust calculation of quantum mechanical {features|attributes|functions|characteristics|capabilities|options} for atoms and molecules.{For a|To get a|For any} benchmark set of 49 experimental molecular polarizabilities, the predictive {power|energy} {of the|from the|in the|on the|with the|of your} presented {method|technique|approach|strategy|system|process} competes against second-order perturbation theory {in a|inside a|within a} converged atomic-orbital basis set at a fraction of its computational {costs|expenses|fees|charges}.Robustness tests {within|inside} a diverse validation set of {more than|greater than} 80,000 molecules show that the calculation of isotropic molecular polarizabilities {has a|features a|includes a} low failure-rate of only 0.{3|three} %.We present {furthermore|moreover|in addition|additionally} a {generally|usually|typically|normally|commonly|frequently} applicable van der Waals radius model {that is|that’s|which is|that is certainly|that is definitely|that may be} rooted on atomic static polarizabilites.Efficiency tests show that such radii can even be calculated for small- to medium-size proteins {where|exactly where} the {largest|biggest} {system|method|program|technique} (SARS-CoV-2 spike protein) has 42,539 atoms.Following the {work|function|perform|operate} of Domingo-Alemenara et al. [Domingo-Alemenara et al., Nat. Comm., 2019, {10|ten}, 5811],we present computational predictions for retention {times|occasions|instances} for {different|various|distinct|diverse|unique|distinctive} chromatographic {methods|techniques|strategies|approaches|procedures|solutions} and describe how physicochemical {features|attributes|functions|characteristics|capabilities|options} {improve|enhance|boost|increase|strengthen} the predictive {power|energy} of machine-learning models that otherwise only {rely on|depend on} two-dimensional {features|attributes|functions|characteristics|capabilities|options} like molecular fingerprints.{Additionally|In addition|Furthermore|Moreover|Also|On top of that}, we {developed|created} an internal benchmark set of experimental super-critical fluid chromatography retention {times|occasions|instances}.{For those|For all those} {methods|techniques|strategies|approaches|procedures|solutions}, improvements of {up to|as much as} 17 % are obtained when combining molecular fingerprints with physicochemical descriptors.Shapley additive explanation values show {furthermore|moreover|in addition|additionally} that the physical nature {of the|from the|in the|on the|with the|of your} applied {features|attributes|functions|characteristics|capabilities|options} {can be|may be|could be|might be|is often|is usually} retained {within the|inside the} final machine-learning models.We {generally|usually|typically|normally|commonly|frequently} {recommend|suggest|advise|advocate|propose} the kallisto framework as a robust, low-cost, and physically motivated featurizer for upcoming state-of-the-art machine-learning {studies|research}. 2072801-99-9 Order 5-Bromo-2-(trifluoromethoxy)pyridine Purity PMID:24455443
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