MOViDA: multiomics visible drug activity prediction with a biologically informed neural network model

Motivation: The process of drug development is inherently complex, marked by extended intervals from the inception of a pharmaceutical agent to its eventual launch in the market. Additionally, each phase in this process is associated with a significant failure rate, amplifying the inherent challenges of this task. Computational virtual screening powered by machine learning algorithms has emerged as a promising approach for predicting therapeutic efficacy. However, the complex relationships between the features learned by these algorithms can be challenging to decipher.Results: We have engineered an artificial neural network model designed specifically for predicting drug sensitivity. This model utilizes a biologically informed visible neural network, thereby enhancing its interpretability. The trained model allows for an in-depth exploration of the biological pathways integral to prediction and the chemical attributes of drugs that impact sensitivity. Our model harnesses multiomics data derived from a different tumor tissue sources, as well as molecular descriptors that encapsulate the properties of drugs. We extended the model to predict drug synergy, resulting in favorable outcomes while retaining interpretability. Given the imbalanced nature of publicly available drug screening datasets, our model demonstrated superior performance to state-of-the-art visible machine learning algorithms.Availability and implementation: MOViDA is implemented in Python using PyTorch library and freely available for download at https://github. com/Luigi-Ferraro/MOViDA. Training data, RIS score and drug features are archived on Zenodo https://doi.org/10.5281/zenodo.8180380

Modelling cytochromes P450 binding modes to predict P450 inhibition, metabolic stability and isoform selectivity

The cytochromes P450 (P450) superfamily is a diverse group of enzymes involved in the metabolism of xenobiotics, whose orientations within the catalytic site can lead to different binding modes, namely productive, nonproductive, and inhibitory. This article collects the most recent approaches that individually study P450- ligand interactions, including a novel in silico technology, developed in the framework of the Human Cytochrome P450 Consortium initiative, that provides reliable in silico predictions of P450 inhibition, metabolic stability and isoform selectivity

Hydrogen bonding interactions of covalently bonded fluorine atoms: from crystallographic data to a new angular function in the GRID force field

Through the years the GRID force field has been tuned to fit experimental observations in crystal structures. This paper describes the determination of the hydrogen bonding pattern for organic fluorines based on an exhaustive inspection of the Protein Data Bank. All the PDB complexes, whose protein structures have cocrystallized fluorine-containing ligands, were examined and geometrically inspected. By applying statistics, the hydrogen bonding geometry was described as a distribution function of the angle at the fluorine: a new specific angular function was consequently defined and inserted in the program GRID to estimate the effect of fluorine hydrogen bonds on the ligand-protein binding. All the fluorine-containing ligands collected from the PDB were docked within their corresponding protein binding sites: introducing the fluorine hydrogen bonding contribution improves the results of the docking experiments in terms of accuracy and ranking

IAP antagonists: promising candidates for cancer therapy

A promising strategy in cancer therapy aims to promote apoptosis in cancer cells. Targeting inhibitor of apoptosis proteins (IAPs) with small-molecule inhibitors has attracted increasing interest in triggering cancer cell death. It is considered to have great potential for cancer drug discovery because IAPs block apoptosis at the core of the apoptotic machinery and are aberrantly expressed in various tumors. This review focuses on the current development of small-molecule IAP antagonists for cancer therapy

Comparison of ligand-based and structure-based 3D-QSAR approaches: a case study on (aryl-)bridged 2-aminobenzonitriles inhibiting HIV-1 reverse transcriptase

Ligand- (GRIND) and structure-based (GLUE/GRIND) 3D-QSAR approaches were compared for 55 (aryl-)bridged 2-aminobenzonitriles inhibiting HIV-1 reverse transcriptase (HIV-1 RT). The ligand-based model was built from conformers selected by in vacuo minimization. The available X-ray structure of 3v in complex with HIV-1 RT allowed comparative structure-based calculations using the new docking software GLUE for conformer selection. Both models were validated via statistics and via virtual receptor sites (VRS) considering pharmacophoric regions and mutual distances, which were also compared with experimental evidence. The statistics show slight superiority of the structure-based approach in terms of fitting and prediction. By encoding relevant molecular interaction fields (MIF) into pharmacophoric regions, 10 such regions were derived from both models; they all fit the real receptor except HBD2. Also mutual distances highly agree between the real site and both VRS. Although distances from the structure-based approach are closer to the real receptor, present data prove the validity of the ligand-based GRIND approach

QSAR modeling and data mining link Torsades de Pointes risk to the interplay of extent of metabolism, active transport, and HERG liability

We collected 1173 hERG patch clamp (PC) data (IC50) from the literature to derive twelve classification models for hERG inhibition, covering a large variety of chemical descriptors and classification algorithms. Models were generated using 545 molecules and validated through 258 external molecules tested in PC experiments. We also evaluated the suitability of the best models to predict the activity of 26 proprietary compounds tested in radioligand binding displacement (RBD). Results proved the necessity to use multiple validation sets for a true estimation of model accuracy and demonstrated that using various descriptors and algorithms improves the performance of ligand-based models. Intriguingly, one of the most accurate models uncovered an unexpected link between extent of metabolism and hERG liability. This hypothesis was fairly reinforced by using the Biopharmaceutics Drug Disposition Classification System (BDDCS) that recognized 94% of the hERG inhibitors as extensively metabolized in vivo. Data mining suggested that high Torsades de Pointes (TdP) risk results from an interplay of hERG inhibition, extent of metabolism, active transport, and possibly solubility. Overall, these new findings might improve both the decision making skills of pharmaceutical scientists to mitigate hERG liability during the drug discovery process and the TdP risk assessment during drug development

Novel TOPP descriptors in 3D-QSAR analysis of apoptosis inducing 4-aryl-4H-chromenes: comparison versus other 2D- and 3D-descriptors

Novel 3D-descriptors using Triplets Of Pharmacophoric Points (TOPP) were evaluated in QSAR-studies on 80 apoptosis-inducing 4-aryl-4H-chromenes. A predictive QSAR model was obtained using PLS, confirmed by means of internal and external validations. Performance of the TOPP approach was compared with that of other 2D- and 3D-descriptors; statistical analysis indicates that TOPP descriptors perform best. A ranking of TOPP > GRIND > BCI 4096 = ECFP > FCFP > GRID-GOLPE >> DRAGON >>> MDL 166 was achieved. Finally, in a 'consensus' analysis predictions obtained using the single methods were compared with an average approach using six out of eight methods. The use of the average is statistically superior to the single methods. Beyond it, the use of several methods can help to easily investigate the presence/absence of outliers according to the 'consensus' of the predicted values: agreement among all the methods indicates a precise prediction, whereas large differences between predicted values (for the same compounds by different methods) would demand caution when using such predictions. (C) 2007 Elsevier Ltd. All rights reserved

Peptide studies by means of principal properties of amino acids derived from MIF descriptors

The paper derives a new set of principal properties (PPs) for coded amino acids from GRID maps and experimental data. The three scales characterize side chains according to their polarity (PP1), size/ hydrophobicity (PP2) and H-bonding capability (PP3) and can be used profitably both for describing and designing peptide series. The new parameters are further used to develop modified auto- and cross-covariance transforms which appear to be even more suitable for the stated goals, as they label each peptide according to its bonding capabilities

Binding studies and GRIND/ALMOND-based 3D QSAR analysis of benzothiazine type K(ATP)-channel openers

For seventeen 1,4-benzothiazine potassium channel openers, we performed binding studies in rat aortic smooth muscle cells and cardiomyocytes, compared their binding affinities with published relaxation data, and derived 3D-QSAR models using GRIND/ALMOND descriptors. Binding affinities in smooth muscle cells range from a pK(D) of 4.76 for compound 3e to 9.10 for compound 4c. Comparison of data for smooth muscle relaxation and binding shows preferentially higher pEC(50)s for the former. In cardiomyocytes, pK(D) values range from 4.21 for 3e to 8.16 for 4c. 3D-QSAR analysis resulted in PLS models of two latent variables for all three activities with determination coefficients of 0.97 (smooth muscle relaxation) and 0.94 (smooth muscle cells- and cardiomyocytes-binding). Internal validation yielded q(2) values of 0.69, 0.66, and 0.64. The carbonyl on the N-4 substituent, the hydrogen bond acceptor at C-6, the five-membered ring at N-4, and the gem-dimethyls mainly guide strong binding and strong smooth muscle relaxation. (c) 2005 Elsevier Ltd. All rights reserved