SimilarityLab:Molecular similarity for sar exploration and target prediction on the web

Exploration of chemical space around hit, experimental, and known active compounds is an important step in the early stages of drug discovery. In academia, where access to chemical synthesis efforts is restricted in comparison to the pharma-industry, hits from primary screens are typically followed up through purchase and testing of similar compounds, before further funding is sought to begin medicinal chemistry efforts. Rapid exploration of druglike similars and structure–activity relationship profiles can be achieved through our new webservice SimilarityLab. In addition to searching for commercially available molecules similar to a query compound, SimilarityLab also enables the search of compounds with recorded activities, generating consensus counts of activities, which enables target and off-target prediction. In contrast to other online offerings utilizing the USRCAT similarity measure, SimilarityLab’s set of commercially available small molecules is consistently updated, currently containing over 12.7 million unique small molecules, and not relying on published databases which may be many years out of date. This ensures researchers have access to up-to-date chemistries and synthetic processes enabling greater diversity and access to a wider area of commercial chemical space. All source code is available in the SimilarityLab source repository

CLAffinity:A software tool for identification of optimum ligand affinity for competition-based primary screens

[Image: see text] A simplistic assumption in setting up a competition assay is that a low affinity labeled ligand can be more easily displaced from a target protein than a high affinity ligand, which in turn produces a more sensitive assay. An often-cited paper correctly rallies against this assumption and recommends the use of the highest affinity ligand available for experiments aiming to determine competitive inhibitor affinities. However, we have noted this advice being applied incorrectly to competition-based primary screens where the goal is optimum assay sensitivity, enabling a clear yes/no binding determination for even low affinity interactions. The published advice only applies to secondary, confirmatory assays intended for accurate affinity determination of primary screening hits. We demonstrate that using very high affinity ligands in competition-based primary screening can lead to reduced assay sensitivity and, ultimately, the discarding of potentially valuable active compounds. We build on techniques developed in our PyBindingCurve software for a mechanistic understanding of complex biological interaction systems, developing the “CLAffinity tool” for simulating competition experiments using protein, ligand, and inhibitor concentrations common to drug screening campaigns. CLAffinity reveals optimum labeled ligand affinity ranges based on assay parameters, rather than general rules to optimize assay sensitivity. We provide the open source CLAffinity software toolset to carry out assay simulations and a video summarizing key findings to aid in understanding, along with a simple lookup table allowing identification of optimal dynamic ranges for competition-based primary screens. The application of our freely available software and lookup tables will lead to the consistent creation of more performant competition-based primary screens identifying valuable hit compounds, particularly for difficult targets

MRlogP:Transfer learning enables accurate logP prediction using small experimental training datasets

Small molecule lipophilicity is often included in generalized rules for medicinal chemistry. These rules aim to reduce time, effort, costs, and attrition rates in drug discovery, allowing the rejection or prioritization of compounds without the need for synthesis and testing. The availability of high quality, abundant training data for machine learning methods can be a major limiting factor in building effective property predictors. We utilize transfer learning techniques to get around this problem, first learning on a large amount of low accuracy predicted logP values before finally tuning our model using a small, accurate dataset of 244 druglike compounds to create MRlogP, a neural network-based predictor of logP capable of outperforming state of the art freely available logP prediction methods for druglike small molecules. MRlogP achieves an average root mean squared error of 0.988 and 0.715 against druglike molecules from Reaxys and PHYSPROP. We have made the trained neural network predictor and all associated code for descriptor generation freely available. In addition, MRlogP may be used online via a web interface

Quantitative microdialysis:Experimental protocol and software for small molecule protein affinity determination and for exclusion of compounds with poor physicochemical properties

Quantitative microdialysis is a traditional biophysical affinity determination technique. In the development of the detailed experimental protocol presented, we used commercially available equipment, rapid equilibrium dialysis (RED) devices (ThermoFisher Scientific), which means that it is open to most laboratories. The target protein and test compound are incubated in a chamber partitioned to allow only small molecules to transition to a larger reservoir chamber, then reversed-phase high performance liquid chromatography (RP-HPLC) or liquid chromatography–mass spectrometry (LC–MS) is used to determine the abundance of compound in each chamber. A higher compound concentration measured in the chamber that contains the target protein indicates binding. As a novel, and differentiating contribution, we present a protocol for mathematical analysis of experimental data. We provide the equations and the software to yield dissociation constants for the test compound-target protein complex up to 0.5 mM KD, and we quantitatively discuss the limitations of affinities in relation to measured compound concentrations

PyBindingCurve, simulation, and curve fitting to complex binding systems at equilibrium

[Image: see text] Understanding multicomponent binding interactions in protein–ligand, protein–protein, and competition systems is essential for fundamental biology and drug discovery. Hand-deriving equations quickly become unfeasible when the number of components is increased, and direct analytical solutions only exist to a certain complexity. To address this problem and allow easy access to simulation, plotting, and parameter fitting to complex systems at equilibrium, we present the Python package PyBindingCurve. We apply this software to explore homodimer and heterodimer formations culminating in the discovery that under certain conditions, homodimers are easier to break with an inhibitor than heterodimers and may also be more readily depleted. This is a potentially valuable and overlooked phenomenon of great importance to drug discovery. PyBindingCurve may be expanded to operate on any equilibrium binding system and allows definition of custom systems using a simple syntax. PyBindingCurve is available under the MIT license at https://github.com/stevenshave/pybindingcurve as the Python source code accompanied by examples and as an easily installable package within the Python Package Index

Evolution and Impact of High Content Imaging

Abstract/outline: The field of high content imaging has steadily evolved and expanded substantially across many industry and academic research institutions since it was first described in the early 1990′s. High content imaging refers to the automated acquisition and analysis of microscopic images from a variety of biological sample types. Integration of high content imaging microscopes with multiwell plate handling robotics enables high content imaging to be performed at scale and support medium- to high-throughput screening of pharmacological, genetic and diverse environmental perturbations upon complex biological systems ranging from 2D cell cultures to 3D tissue organoids to small model organisms. In this perspective article the authors provide a collective view on the following key discussion points relevant to the evolution of high content imaging:• Evolution and impact of high content imaging: An academic perspective• Evolution and impact of high content imaging: An industry perspective• Evolution of high content image analysis• Evolution of high content data analysis pipelines towards multiparametric and phenotypic profiling applications• The role of data integration and multiomics• The role and evolution of image data repositories and sharing standards• Future perspective of high content imaging hardware and softwar

Identification and X-ray Co-crystal Structure of a Small-Molecule Activator of LFA-1-ICAM-1 Binding

The integrin Leucocyte function associated antigen 1 (LFA-1) is a heterodimeric immune receptor ubiquitously expressed on all leucocytes. Its interaction with Intercellular adhesion molecule 1 (ICAM-1) provides a critical recognition event between T-cells and antigen presenting cells in the immune systems efforts to pull off an early stage cell mediated immune response.[1–3] The LFA-1/ICAM-1 axis has thus been explored as a target interaction for drug discovery.[4–7] Furthermore, the structural changes of LFA-1 upon activation and interaction with ICAM-1 also make the LFA-1/ICAM-1 interaction an interesting example of protein-protein interaction (PPI) inhibition by small molecule inhibitors.[8,9] While protein-protein interaction inhibition by small molecules is considered to be the ultimate art in drug design, even fewer examples of true agonists of PPIs have been reported.[10–12] As for LFA-1, such activators would have interesting applications in rare hereditary genetic disorders called Leucocyte adhesion deficiency (LAD) or as potential enhancers of tumour immunotherapy.[13,14] Although, one such activator has been described recently, closer biological investigation has shown that it ultimately worked as an inhibitor on a cellular level by locking the LFA-1/ICAM-1 interaction when reversibility was needed for detachment of immune cells from endothelial surfaces and tissue infiltration.[15] Herein we describe the identification and structural biology of IBE-667, an ICAM-1 binding enhancer for LFA-1 from on-bead screening of tagged one-bead one-compound combinatorial libraries by confocal nanoscanning and bead picking (CONA).[16] Cellular assays demonstrate the activity of IBE-667 in promoting the binding of LFA-1 on activated immune cells to ICAM-1. X-ray structure based analysis did not only allow us to explain the molecular features of IBE-667 binding to LFA-1 but also offers an explanation for its mode of action

Diclofenac identified as a kynurenine 3-monooxygenase binder and inhibitor by molecular similarity techniques

In this study, we apply a battery of molecular similarity techniques to known inhibitors of kynurenine 3-monooxygenase (KMO), querying each against a repository of approved, experimental, nutraceutical, and illicit drugs. Four compounds are assayed against KMO. Subsequently, diclofenac (also known by the trade names Voltaren, Voltarol, Aclonac, and Cataflam) has been confirmed as a human KMO protein binder and inhibitor in cell lysate with low micromolar <i>K</i>_D and IC₅₀, respectively, and low millimolar cellular IC₅₀. Hit to drug hopping, as exemplified here for one of the most successful anti-inflammatory medicines ever invented, holds great promise for expansion into new disease areas and highlights the not-yet-fully-exploited potential of drug repurposing