scholarly journals Anion-Cation Contrast of Small Molecule Solvation in Salt Solutions

2022 ◽  
Author(s):  
Stefan Hervø-Hansen ◽  
Jan Heyda ◽  
Mikael Lund ◽  
Nobuyuki Matubayasi
Keyword(s):  
Computational Methods ◽  
Small Molecule ◽  
Salt Solutions ◽  
Molecular Systems ◽  
Anions And Cations

Salts are inseparable in their perturbation of molecular systems by experimental and computational methods, rendering it difficult to dissect the effects exerted by the anions and cations individually. Here we...

scholarly journals Open Force Field Consortium: Escaping atom types using direct chemical perception with SMIRNOFF v0.1

2018 ◽  
Author(s):  
David L. Mobley ◽  
Caitlin C. Bannan ◽  
Andrea Rizzi ◽  
Christopher I. Bayly ◽  
John D. Chodera ◽  
...  
Keyword(s):  
Force Field ◽  
Molecular Mechanics ◽  
Small Molecule ◽  
Force Fields ◽  
Chemical Space ◽  
Dielectric Constants ◽  
Intermediate Step ◽  
Amber Force Field ◽  
Molecular Systems ◽  
Chemical Perception

AbstractHere, we focus on testing and improving force fields for molecular modeling, which see widespread use in diverse areas of computational chemistry and biomolecular simulation. A key issue affecting the accuracy and transferrability of these force fields is the use of atom typing. Traditional approaches to defining molecular mechanics force fields must encode, within a discrete set of atom types, all information which will ever be needed about the chemical environment; parameters are then assigned by looking up combinations of these atom types in tables. This atom typing approach leads to a wide variety of problems such as inextensible atom-typing machinery, enormous difficulty in expanding parameters encoded by atom types, and unnecessarily proliferation of encoded parameters. Here, we describe a new approach to assigning parameters for molecular mechanics force fields based on the industry standard SMARTS chemical perception language (with extensions to identify specific atoms available in SMIRKS). In this approach, each force field term (bonds, angles, and torsions, and nonbonded interactions) features separate definitions assigned in a hierarchical manner without using atom types. We accomplish this using direct chemical perception, where parameters are assigned directly based on substructure queries operating on the molecule(s) being parameterized, thereby avoiding the intermediate step of assigning atom types — a step which can be considered indirect chemical perception. Direct chemical perception allows for substantial simplification of force fields, as well as additional generality in the substructure queries. This approach is applicable to a wide variety of (bio)molecular systems, and can greatly reduce the number of parameters needed to create a complete force field. Further flexibility can also be gained by allowing force field terms to be interpolated based on the assignment of fractional bond orders via the same procedure used to assign partial charges. As an example of the utility of this approach, we provide a minimalist small molecule force field derived from Merck’s parm@Frosst (an Amber parm99 descendant), in which a parameter definition file only ≈ 300 lines long can parameterize a large and diverse spectrum of pharmaceutically relevant small molecule chemical space. We benchmark this minimalist force field on the FreeSolv small molecule hydration free energy set and calculations of densities and dielectric constants from the ThermoML Archive, demonstrating that it achieves comparable accuracy to the Generalized Amber Force Field (GAFF) that consists of many thousands of parameters.

scholarly journals Identification of Novel Potential VEGFR-2 Inhibitors Using a Combination of Computational Methods for Drug Discovery

Life ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1070
Author(s):  
Mohammad M. Al-Sanea ◽  
Garri Chilingaryan ◽  
Narek Abelyan ◽  
Arsen Sargsyan ◽  
Sargis Hovhannisyan ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Computational Methods ◽  
Small Molecule ◽  
Small Molecule Inhibitors ◽  
Acquired Resistance ◽  
Growth Factor Receptor ◽  
Clinical Effects ◽  
Wide Range ◽  
Critical Issues ◽  
Endothelial Growth Factor

The vascular endothelial growth factor receptor 2 (VEGFR-2) is largely recognized as a potent therapeutic molecular target for the development of angiogenesis-related tumor treatment. Tumor growth, metastasis and multidrug resistance highly depends on the angiogenesis and drug discovery of the potential small molecules targeting VEGFR-2, with the potential anti-angiogenic activity being of high interest to anti-cancer research. Multiple small molecule inhibitors of the VEGFR-2 are approved for the treatment of different type of cancers, with one of the most recent, tivozanib, being approved by the FDA for the treatment of relapsed or refractory advanced renal cell carcinoma (RCC). However, the endogenous and acquired resistance of the protein, toxicity of compounds and wide range of side effects still remain critical issues, which lead to the short-term clinical effects and failure of antiangiogenic drugs. We applied a combination of computational methods and approaches for drug design and discovery with the goal of finding novel, potential and small molecule inhibitors of VEGFR2, as alternatives to the known inhibitors’ chemical scaffolds and components. From studying several of these compounds, the derivatives of pyrido[1,2-a]pyrimidin-4-one and isoindoline-1,3-dione in particular were identified.

scholarly journals How reliable are ligand-centric methods for Target Fishing?

2015 ◽  
Author(s):  
Antonio Peón ◽  
Cuong C. Dang ◽  
Pedro J. Ballester
Keyword(s):  
Side Effects ◽  
Computational Methods ◽  
Small Molecule ◽  
Target Prediction ◽  
Predictive Performance ◽  
Control Group ◽  
Substantial Body ◽  
Small Molecule Drugs ◽  
Approved Drugs ◽  
Target Fishing

Computational methods for Target Fishing (TF), also known as Target Prediction or Polypharmacology Prediction, can be used to discover new targets in small-molecule drugs. This may result in repositioning the drug in a new indication or improving our current understanding of its efficacy and side effects. While there is a substantial body of research on TF methods, there is still a need to improve their validation, which is often limited to a small part of the available targets and not easily interpretable by the user. Here we discuss how target-centric TF methods are inherently limited by the number of targets that can possibly predict (this number is by construction much larger in ligand-centric techniques). We also propose a new benchmark to validate TF methods, which is particularly suited to analyse how predictive performance varies with the query molecule. On average over approved drugs, we estimate that only five predicted targets will have to be tested to find two true targets with submicromolar potency (a strong variability in performance is however observed). In addition, we find that an approved drug has currently an average of eight known targets, which reinforces the notion that polypharmacology is a common and strong event. Furthermore, with the assistance of a control group of randomly-selected molecules, we show that the targets of approved drugs are generally harder to predict.

scholarly journals Chalcogen-Nitrogen Bond: Insights into a Key Chemical Motif

Catalysts ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 114
Author(s):  
Marco Bortoli ◽  
Andrea Madabeni ◽  
Pablo Andrei Nogara ◽  
Folorunsho B. Omage ◽  
Giovanni Ribaudo ◽  
...  
Keyword(s):  
Organic Chemistry ◽  
Computational Methods ◽  
Inorganic Materials ◽  
Molecular Systems ◽  
Nitrogen Nucleus ◽  
Intramolecular Cyclizations ◽  
Valuable Role ◽  
Nitrogen Bond

Chalcogen-nitrogen chemistry deals with systems in which sulfur, selenium, or tellurium is linked to a nitrogen nucleus. This chemical motif is a key component of different functional structures, ranging from inorganic materials and polymers, to rationally designed catalysts, to bioinspired molecules and enzymes. The formation of a selenium–nitrogen bond, typically occurring upon condensation of an amine and the unstable selenenic acid, often leading to intramolecular cyclizations, and its disruption, mainly promoted by thiols, are rather common events in organic Se-catalyzed processes. In this work, focusing on examples taken from selenium organic chemistry and biochemistry, the selenium–nitrogen bond is described, and its strength and reactivity are quantified using accurate computational methods applied to model molecular systems. The intermediate strength of the Se–N bond, which can be tuned to necessity, gives rise to significant trends when comparing it to the stronger S– and weaker Te–N bonds, reaffirming also in this context the peculiar and valuable role of selenium in chemistry and life.

Predicting the Solubility of the Anti-Cancer Agent Docetaxel in Small Molecule Excipients using Computational Methods

2007 ◽  
Vol 25 (1) ◽  
pp. 147-157 ◽  
Author(s):  
Loan Huynh ◽  
Justin Grant ◽  
Jean-Christophe Leroux ◽  
Pascal Delmas ◽  
Christine Allen
Keyword(s):  
Computational Methods ◽  
Small Molecule ◽  
Anti Cancer ◽  
Cancer Agent

Computational methods for small molecule identification

2019 ◽  
Vol 61 (5-6) ◽  
pp. 285-292
Author(s):  
Kai Dührkop
Keyword(s):  
Mass Spectrometry ◽  
Tandem Mass Spectrometry ◽  
Statistical Model ◽  
Small Molecules ◽  
Computational Methods ◽  
Small Molecule ◽  
Mass Spectra ◽  
Tandem Mass ◽  
Molecular Fingerprint ◽  
Fragmentation Tree

Abstract Identification of small molecules remains a central question in analytical chemistry, in particular for natural product research, metabolomics, environmental research, and biomarker discovery. Mass spectrometry is the predominant technique for high-throughput analysis of small molecules. But it reveals only information about the mass of molecules and, by using tandem mass spectrometry, about the mass of molecular fragments. Automated interpretation of mass spectra is often limited to searching in spectral libraries, such that we can only dereplicate molecules for which we have already recorded reference mass spectra. In my thesis “Computational methods for small molecule identification” we developed SIRIUS, a tool for the structural elucidation of small molecules with tandem mass spectrometry. The method first computes a hypothetical fragmentation tree using combinatorial optimization. By using a Bayesian statistical model, we can learn parameters and hyperparameters of the underlying scoring directly from data. We demonstrate that the statistical model, which was fitted on a small dataset, generalizes well across many different datasets and mass spectrometry instruments. In a second step the fragmentation tree is used to predict a molecular fingerprint using kernel support vector machines. The predicted fingerprint can be searched in a structure database to identify the molecular structure. We demonstrate that our machine learning model outperforms all other methods for this task, including its predecessor FingerID. SIRIUS is available as commandline tool and as user interface. The molecular fingerprint prediction is implemented as web service and receives over one million requests per month.

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