Late stage Functionalization automation of MS driven structure elucidation

Solutions for Chemistry

Late Stage Functionalization studies aims to synthetize chemical analogs without the need of using intermediate functional groups. In most cases, conditions to synthetize the desired product should be tuned and the analysis of all the obtained data can be quite time consuming. MassChemSite and WebChembase tandem offers a solution to speed up the process by automatizing the data processing and summarizing the extracted information. In this talk we will show an example of C-H late stage functionalization using a set of catalysts and solvents to synthesize halogenation analogs.

Chemically-Aware molecular and atomic descriptors for medicinal chemistry applications

Solutions for Computational Chemist

Molecular interaction field (MIF), aiming at describing molecules as their ability to interact with any chemical entity, and related molecular descriptors are one of the most established and versatile concepts in drug discovery. Clearly, any further improvement in the in silico molecular description is highly desirable for novel and more reliable drug design and medicinal chemistry applications. Here we applied a hybrid quantum mechanics and machine learning approach to better parameterize the hydrogen-bond potentials of small molecules which allowed us to derive more chemical-aware force fields and MIFs. Finally, we derived new molecular and atomic descriptors, that will be available in VolSurf3, and we will show examples of their usability in drug design and medicinal chemistry applications.

Introduction to the webinars

Our vision for Mass Spec Software development

Introduction to our Practical Applications for Drug Discovery 2020 Webinar videos.

Structure-metabolism relationships in human- AOX: Chemical insights from a large database of aza-aromatic and amide compounds

Structure-metabolism relationships in human- AOX: Chemical insights from a large database of aza-aromatic and amide compounds

April 2018.

Lepri S, Ceccarelli M, Milani N, Tortorella S, Cucco A, Valeri A, Goracci L, Brink A, Cruciani G.

Abstract

Aldehyde oxidase (AOX) is a metabolic enzyme catalyzing the oxidation of aldehyde and aza-aromatic compounds and the hydrolysis of amides, moieties frequently shared by the majority of drugs. Despite its key role in human metabolism, to date only fragmentary information about the chemical features responsible for AOX susceptibility are reported and only “very local” structure-metabolism relationships based on a small number of similar compounds have been developed.

This study reports a more comprehensive coverage of the chemical space of structures with a high risk of AOX phase I metabolism in humans. More than 270 compounds were studied to identify the site of metabolism and the metabolite(s). Both electronic [supported by density functional theory (DFT) calculations] and exposure effects were considered when rationalizing the structure-metabolism relationship. 

From Experiments to a Fast Easy-to-Use Computational Methodology to Predict Human Aldehyde Oxidase Selectivity and Metabolic Reactions

From Experiments to a Fast Easy-to-Use Computational Methodology to Predict Human Aldehyde Oxidase Selectivity and Metabolic Reactions

January 2018.

Cruciani G, Milani N, Benedetti P, Lepri S, Cesarini L, Baroni M, Spyrakis F, Tortorella S, Mosconi E, Goracci L.

Abstract

Aldehyde oxidase (AOX) is a molibdo-flavoenzyme that has raised great interest in recent years, since its contribution in xenobiotic metabolism has not always been identified before clinical trials, with consequent negative effects on the fate of new potential drugs. The fundamental role of AOX in metabolizing xenobiotics is also due to the attempt of medicinal chemists to stabilize candidates toward cytochrome P450 activity, which increases the risk for new compounds to be susceptible to AOX nucleophile attack.

Therefore, novel strategies to predict the potential liability of new entities toward the AOX enzyme are urgently needed to increase effectiveness, reduce costs, and prioritize experimental studies. In the present work, we present the most up-to-date computational method to predict liability toward human AOX (hAOX), for applications in drug design and pharmacokinetic optimization. The method was developed using a large data set of homogeneous experimental data, which is also disclosed as Supporting Information.