Disease-Specific Differentiation Between Drugs and Non-Drugs Using Principal Component Analysis of Their Molecular Descriptor Space

Current Trends and Challenges in Drug-Likeness Prediction: Are They Generalizable and Interpretable?

Importance: Drug-likeness of a compound is an overall assessment of its potential to succeed in clinical trials, and is essential for economizing research expenditures by filtering compounds with unfavorable properties and poor development potential. To this end, a robust drug-likeness prediction method is indispensable. Various approaches, including discriminative rules, statistical models, and machine learning models, have been developed to predict drug-likeness based on physiochemical properties and structural features. Notably, recent advancements in novel deep learning techniques have significantly advanced drug-likeness prediction, especially in classification performance. Highlights: In this review, we addressed the evolving landscape of drug-likeness prediction, with emphasis on methods employing novel deep learning techniques, and highlighted the current challenges in drug-likeness prediction, specifically regarding the aspects of generalization and interpretability. Moreover, we explored potential remedies and outlined promising avenues for future research. Conclusion: Despite the hurdles of generalization and interpretability, novel deep learning techniques have great potential in drug-likeness prediction and are worthy of further research efforts.

Novel Drug-Like Somatostatin Receptor 4 Agonists are Potential Analgesics for Neuropathic Pain

Somatostatin released from the capsaicin-sensitive sensory nerves mediates analgesic and anti-inflammatory effects via the somatostatin sst4 receptor without endocrine actions. Therefore, sst4 is considered to be a novel target for drug development in pain including chronic neuropathy, which is an emerging unmet medical need. Here, we examined the in silico binding, the sst4-linked G-protein activation on stable receptor expressing cells (1 nM to 10 μM), and the effects of our novel pyrrolo-pyrimidine molecules in mouse inflammatory and neuropathic pain models. All four of the tested compounds (C1-C4) bind to the same binding site of the sst4 receptor with similar interaction energy to high-affinity reference sst4 agonists, and they all induce G-protein activation. C1 is the more efficacious (γ-GTP-binding: 218.2% ± 36.5%) and most potent (EC50: 37 nM) ligand. In vivo testing of the actions of orally administered C1 and C2 (500 µg/kg) showed that only C1 decreased the resiniferatoxin-induced acute neurogenic inflammatory thermal allodynia and mechanical hyperalgesia significantly. Meanwhile, both of them remarkably reduced partial sciatic nerve ligation-induced chronic neuropathic mechanical hyperalgesia after a single oral administration of the 500 µg/kg dose. These orally active novel sst4 agonists exert potent anti-hyperalgesic effect in a chronic neuropathy model, and therefore, they can open promising drug developmental perspectives.

Data Mining and Machine Learning Models for Predicting Drug Likeness and Their Disease or Organ Category

Data mining approaches can uncover underlying patterns in chemical and pharmacological property space decisive for drug discovery and development. Two of the most common approaches are visualization and machine learning methods. Visualization methods use dimensionality reduction techniques in order to reduce multi-dimension data into 2D or 3D representations with a minimal loss of information. Machine learning attempts to find correlations between specific activities or classifications for a set of compounds and their features by means of recurring mathematical models. Both models take advantage of the different and deep relationships that can exist between features of compounds, and helpfully provide classification of compounds based on such features or in case of visualization methods uncover underlying patterns in the feature space. Drug-likeness has been studied from several viewpoints, but here we provide the first implementation in chemoinformatics of the t-Distributed Stochastic Neighbor Embedding (t-SNE) method for the visualization and the representation of chemical space, and the use of different machine learning methods separately and together to form a new ensemble learning method called AL Boost. The models obtained from AL Boost synergistically combine decision tree, random forests (RF), support vector machine (SVM), artificial neural network (ANN), k nearest neighbors (kNN), and logistic regression models. In this work, we show that together they form a predictive model that not only improves the predictive force but also decreases bias. This resulted in a corrected classification rate of over 0.81, as well as higher sensitivity and specificity rates for the models. In addition, separation and good models were also achieved for disease categories such as antineoplastic compounds and nervous system diseases, among others. Such models can be used to guide decision on the feature landscape of compounds and their likeness to either drugs or other characteristics, such as specific or multiple disease-category(ies) or organ(s) of action of a molecule.

Indexing Natural Products for Their Potential Anti-Diabetic Activity: Filtering and Mapping Discriminative Physicochemical Properties

Diabetes mellitus (DM) poses a major health problem, for which there is an unmet need to develop novel drugs. The application of in silico techniques and optimization algorithms is instrumental to achieving this goal. A set of 97 approved anti-diabetic drugs, representing the active domain, and a set of 2892 natural products, representing the inactive domain, were used to construct predictive models and to index anti-diabetic bioactivity. Our recently-developed approach of ‘iterative stochastic elimination’ was utilized. This article describes a highly discriminative and robust model, with an area under the curve above 0.96. Using the indexing model and a mix ratio of 1:1000 (active/inactive), 65% of the anti-diabetic drugs in the sample were captured in the top 1% of the screened compounds, compared to 1% in the random model. Some of the natural products that scored highly as potential anti-diabetic drug candidates are disclosed. One of those natural products is caffeine, which is noted in the scientific literature as having the capability to decrease blood glucose levels. The other nine phytochemicals await evaluation in a wet lab for their anti-diabetic activity. The indexing model proposed herein is useful for the virtual screening of large chemical databases and for the construction of anti-diabetes focused libraries.

Improving the use of ranking in virtual screening against HIV-1 integrase with triangular numbers and including ligand profiling with antitargets

A delicate balance exists between a drug molecule's toxicity and its activity. Indeed, efficacy, toxicity, and side effect problems are a common cause for the termination of drug candidate compounds and development projects. To address this, an antitarget interaction profile is built and combined with virtual screening and cross docking for new inhibitors of HIV-1 integrase, in order to consider possible off-target interactions as early as possible in a drug or hit discovery program. New ranking techniques using triangular numbers improve ranking information on the compounds and recovery of known inhibitors into the top compounds using different docking programs. This improved ranking arises from using consensus of ranks between docking programs and ligand efficiencies to derive a new rank, instead of using absolute score values, or average of ranks. The triangular number rerank also allowed the objective combination of results from several protein targets or screen conditions and several programs. Triangular number reranking conserves more information than other reranking methods such as average of scores or averages of ranks. In addition, the use of triangular numbers for reranking makes possible the use of thresholds with a justified leeway based on the number of available known inhibitors, so that the majority of the compounds above the threshold in ranks compare to the compounds that have known experimentally determined biological activity. The battery of anti- or off-targets can be tailored to specific molecular or drug design challenges. In silico filters can thus be deployed in successive stages, for prefiltering, activity profiling, and for further analysis and triaging of libraries of compounds.

Drugs, non-drugs, and disease category specificity: organ effects by ligand pharmacology

Important understanding can be gained from using molecular biology-based and chemistry-based techniques together. Bayesian classifiers have thus been developed in the present work using several statistically significant molecular properties of compiled datasets of drugs and non-drugs, including their disease category or organ. The results show they provide a useful classification and simplicity of several different ligand efficiencies and molecular properties. Early recall of drugs among non-drugs using the classifiers as a ranking tool is also provided. As the chemical space of compounds is addressed together with their anatomical characterization, chemical libraries can be improved to select for specific organ or disease. Eventually, by including even finer detail, the method may help in designing libraries with specific pharmacological or toxicological target chemical space. Alternatively, a lack of statistically significant differences in property density distributions may help in further describing compounds with possibility of activity on several organs or disease groups, and given their very similar or considerably overlapping chemical space, therefore wanted or unwanted side-effects. The overlaps between densities for several properties of organs or disease categories were calculated by integrating the area under the curves where they intersect. The naïve Bayesian classifiers are readily built, fast to score, and easily interpretable.

DrugLogit: logistic discrimination between drugs and nondrugs including disease-specificity by assigning probabilities based on molecular properties

The increasing knowledge of both structure and activity of compounds provides a good basis for enhancing the pharmacological characterization of chemical libraries. In addition, pharmacology can be seen as incorporating both advances from molecular biology as well as chemical sciences, with innovative insight provided from studying target-ligand data from a ligand molecular point of view. Predictions and profiling of libraries of drug candidates have previously focused mainly on certain cases of oral bioavailability. Inclusion of other administration routes and disease-specificity would improve the precision of drug profiling. In this work, recent data are extended, and a probability-based approach is introduced for quantitative and gradual classification of compounds into categories of drugs/nondrugs, as well as for disease- or organ-specificity. Using experimental data of over 1067 compounds and multivariate logistic regressions, the classification shows good performance in training and independent test cases. The regressions have high statistical significance in terms of the robustness of coefficients and 95% confidence intervals provided by a 1000-fold bootstrapping resampling. Besides their good predictive power, the classification functions remain chemically interpretable, containing only one to five variables in total, and the physicochemical terms involved can be easily calculated. The present approach is useful for an improved description and filtering of compound libraries. It can also be applied sequentially or in combinations of filters, as well as adapted to particular use cases. The scores and equations may be able to suggest possible routes for compound or library modification. The data is made available for reuse by others, and the equations are freely accessible at http://hermes.chem.ut.ee/~alfx/druglogit.html.