QSAR modeling: where have you been? Where are you going to?

OOMMPPAA: a tool to aid directed synthesis by the combined analysis of activity and structural data

There is an ever increasing resource in terms of both structural information and activity data for many protein targets. In this paper we describe OOMMPPAA, a novel computational tool designed to inform compound design by combining such data. OOMMPPAA uses 3D matched molecular pairs to generate 3D ligand conformations. It then identifies pharmacophoric transformations between pairs of compounds and associates them with their relevant activity changes. OOMMPPAA presents this data in an interactive application providing the user with a visual summary of important interaction regions in the context of the binding site. We present validation of the tool using openly available data for CDK2 and a GlaxoSmithKline data set for a SAM-dependent methyl-transferase. We demonstrate OOMMPPAA's application in optimizing both potency and cell permeability and use OOMMPPAA to highlight nuanced and cross-series SAR. OOMMPPAA is freely available to download at http://oommppaa.sgc.ox.ac.uk/OOMMPPAA/ .

Computationally driven drug discovery meeting-3 - Verona (Italy): 4 - 6th of March 2014

The following article reports on the results and the outcome of a meeting organised at the Aptuit Auditorium in Verona (Italy), which highlighted the current applications of state-of-the-art computational science to drug design in Italy. The meeting, which had > 100 people in attendance, consisted of over 40 presentations and included keynote lectures given by world-renowned speakers. The topics included in the meeting are areas related to ligand and structure-based ligand design and library design and screening; it also provided discussion pertaining to chemometrics. The meeting also stressed the importance of public-private collaboration and reviewed the different approaches to computationally driven drug discovery taken within academia and industry. The meeting helped define the current position of state-of-the-art computational drug discovery in Italy, pointing out criticalities and assets. This kind of focused meeting is important in the sense that it lends the opportunity of a restricted yet representative community of fellow professionals to deeply discuss the current methodological approaches and provide future perspectives for computationally driven drug discovery.

QSAR design of triazolopyridine mGlu2 receptor positive allosteric modulators

Two QSAR approaches were applied to assist the design and to prioritise the synthesis of new active mGlu2 receptor positive allosteric modulators (PAMs). With the aim to explore a particular point of substitution the models successfully prioritised molecules originating from chemistry ideas and a large virtual library. The two methods, 3D topomer CoMFA and support vector machines with 2D ECFP6 fingerprints, delivered good correlation and success in this prospective application. Fourteen molecules with different substituent decoration were identified by the in silico models and synthesised. They were found to be highly active and their mGlu2 receptor PAM activity (pEC50) was predicted within 0.3 and 0.4log units of error with the two methods. The value of the molecules and the models for the future of the project is discussed.

Industrial applications of in silico ADMET

Quantitative structure activity relationship (QSAR) modeling has been in use for several decades now. One branch of it, in silico ADMET, became more and more important since the late 1990s as studies indicated that poor pharmacokinetics and toxicity were important causes of costly late-stage failures in drug development. In this paper we describe some of the available methods and best practice for the different stages of the in silico model building process. We also describe some more recent developments, like automated model building and the prediction probability. Finally we will discuss the use of in silico ADMET for "big data" and the importance and possible further development of interpretable models.

Drug-Like Protein-Protein Interaction Modulators: Challenges and Opportunities for Drug Discovery and Chemical Biology

[Formula: see text] Fundamental processes in living cells are largely controlled by macromolecular interactions and among them, protein-protein interactions (PPIs) have a critical role while their dysregulations can contribute to the pathogenesis of numerous diseases. Although PPIs were considered as attractive pharmaceutical targets already some years ago, they have been thus far largely unexploited for therapeutic interventions with low molecular weight compounds. Several limiting factors, from technological hurdles to conceptual barriers, are known, which, taken together, explain why research in this area has been relatively slow. However, this last decade, the scientific community has challenged the dogma and became more enthusiastic about the modulation of PPIs with small drug-like molecules. In fact, several success stories were reported both, at the preclinical and clinical stages. In this review article, written for the 2014 International Summer School in Chemoinformatics (Strasbourg, France), we discuss in silico tools (essentially post 2012) and databases that can assist the design of low molecular weight PPI modulators (these tools can be found at www.vls3d.com). We first introduce the field of protein-protein interaction research, discuss key challenges and comment recently reported in silico packages, protocols and databases dedicated to PPIs. Then, we illustrate how in silico methods can be used and combined with experimental work to identify PPI modulators.

Towards predictive resistance models for agrochemicals by combining chemical and protein similarity via proteochemometric modelling

Resistance to pesticides is an increasing problem in agriculture. Despite practices such as phased use and cycling of ‘orthogonally resistant’ agents, resistance remains a major risk to national and global food security. To combat this problem, there is a need for both new approaches for pesticide design, as well as for novel chemical entities themselves. As summarized in this opinion article, a technique termed ‘proteochemometric modelling’ (PCM), from the field of chemoinformatics, could aid in the quantification and prediction of resistance that acts via point mutations in the target proteins of an agent. The technique combines information from both the chemical and biological domain to generate bioactivity models across large numbers of ligands as well as protein targets. PCM has previously been validated in prospective, experimental work in the medicinal chemistry area, and it draws on the growing amount of bioactivity information available in the public domain. Here, two potential applications of proteochemometric modelling to agrochemical data are described, based on previously published examples from the medicinal chemistry literature.

Is regression through origin useful in external validation of QSAR models?

The external validation of QSAR models is crucial to ensure their reliability for assessing new chemicals. The most widely used criteria for external validations, which has been applied in hundreds of more recent QSAR studies are the Golbraikh-Tropsha and Roy methods which these criteria are based on the regression through origin (RTO). In this study, the calculations of the deviation parameters such as absolute errors are used for ascertaining the difference between training and test sets to evaluate the prediction capability of the models. However, these results were not in a good agreement with the proposed criteria for external validation and there is an inconsistency in the definition and calculation of r(2) of RTO and therefore the constructed criteria based on RTO is not optimal. Instead, the calculation of model errors for training and test sets and compare them, provide a possible reliable method to external validation of QSAR models.

Endocrine disruptome--an open source prediction tool for assessing endocrine disruption potential through nuclear receptor binding

Predicting the endocrine disruption potential of compounds is a daunting but essential task. Here we report a new tool for this purpose that we have termed Endocrine Disruptome. It is a free and simple-to-use Web service that runs on an open source platform called Docking interface for Target Systems (DoTS). The molecular docking is handled via AutoDock Vina. Compounds are docked to 18 integrated and well-validated crystal structures of 14 different human nuclear receptors: androgen receptor; estrogen receptors α and β; glucocorticoid receptor; liver X receptors α and β; mineralocorticoid receptor; peroxisome proliferator activated receptors α, β/δ, and γ; progesterone receptor; retinoid X receptor α; and thyroid receptors α and β. Endocrine Disruptome is free of charge and available at http://endocrinedisruptome.ki.si.

Translation of off-target effects: prediction of ADRs by integrated experimental and computational approach

Adverse drug reactions (ADRs) are associated with most drugs, often discovered late in drug development and sometimes only during extended course of clinical use.

Insights into the adsorption of simple benzene derivatives on carbon nanotubes

This work characterizes the adsorption characteristics of simple benzene derivatives on carbon nanotubes.