Computer-Aided Classification of New Psychoactive Substances

The appearance on the free market of synthetic cannabinoids raised the researchers’ interest in establishing their molecular similarity by QSAR analysis. A rigorous criterion for classifying drugs is their chemical structure. Therefore, this article presents the structural similarity of two groups of drugs: benzoylindoles and phenylacetylindoles. Statistical analysis and clustering of the molecules are performed based on their numerical characteristics extracted using Cheminformatics methods. Their similarities/dissimilarities are emphasized using the dendrograms and heat map. The highest discrepancies are found in the phenylacetylindoles group.

A Review on Some Indigenous Medicinal Plants and their Key Applications

In India, 7,500 plant species out of 17,000 are officially integrated in ayurvedic pharmacopeia for over a millennium. There are many industrial uses of medicinal plants which include traditional medicines, phytopharmaceuticals, herbal teas, health food etc. Now days, in silico approaches have been developed which is used in virtual screening and analysis of medicinal plants to be used pharmacologically. It is a cost effective and efficient way for the production of new drugs which is done in three basic steps i.e. molecular docking, developing pharmacophores and determining molecular similarity in shape. WHO has also acknowledged the importance of medicinal plants and has created various guidelines and strategies to encourage its use. Agro industrial technologies also encourage the use of medicinal plants. India has wide variety of plant species in its ecosystem. Out of 17,000 species of plants 7,500 species are used as medicinal plants by the tribal groups, villagers and in traditional medicinal systems like Ayurveda. The aim of the review is to summarize the information on the recent development in the field of medicinal plants and their key applications.

Intelligent Molecular Identification for High Performance Organosulfide Capture Using Active Machine Learning Algorithm

Machine learning and computer-aided approaches significantly accelerate molecular design and discovery in scientific and industrial fields increasingly relying on data science for efficiency. The typical method used is supervised learning which needs huge datasets. Semi-supervised machine learning approaches are effective to train unlabeled data with improved modeling performance, whereas they are limited by the accumulation of prediction errors. Here, to screen solvents for removal of methyl mercaptan, a type of organosulfur impurities in natural gas, we constructed a computational framework by integrating molecular similarity search and active learning methods, namely, molecular active selection machine learning (MASML). This new model framework identifies the optimal molecules set by molecular similarity search and iterative addition to the training dataset. Among all 126,068 compounds in the initial dataset, 3 molecules were identified to be promising for methyl mercaptan (MeSH) capture, including benzylamine (BZA), p-methoxybenzylamine (PZM), and N,N-diethyltrimethylenediamine (DEAPA). Further experiments confirmed the effectiveness of our modeling framework in efficient molecular design and identification for capturing methyl mercaptan, in which DEAPA presents a Henry's law constant 89.4% lower than that of methyl diethanolamine (MDEA).

Dissimilar Ligands Bind in a Similar Fashion: A Guide to Ligand Binding-Mode Prediction with Application to CELPP Studies

The molecular similarity principle has achieved great successes in the field of drug design/discovery. Existing studies have focused on similar ligands, while the behaviors of dissimilar ligands remain unknown. In this study, we developed an intercomparison strategy in order to compare the binding modes of ligands with different molecular structures. A systematic analysis of a newly constructed protein–ligand complex structure dataset showed that ligands with similar structures tended to share a similar binding mode, which is consistent with the Molecular Similarity Principle. More importantly, the results revealed that dissimilar ligands can also bind in a similar fashion. This finding may open another avenue for drug discovery. Furthermore, a template-guiding method was introduced for predicting protein–ligand complex structures. With the use of dissimilar ligands as templates, our method significantly outperformed the traditional molecular docking methods. The newly developed template-guiding method was further applied to recent CELPP studies.

Molecular profile to cancer cell line matchmaking

Profile-to-cell line matchmaking is a computational protocol to identify cancer cell lines that are genomically similar to a patient’s case profile. In doing so, high-throughput drug screens applied to the same cancer cell lines may be used for therapeutic hypothesis generation in research settings and potentially in clinical settings. To evaluate the metrics of the matchmaking, a hold-one-out approach of the considered cancer cell lines is applied, and molecular similarity models are assessed based on their ability to identify cancer cell lines that share therapeutic sensitivity.

AI Informed Toxicity Screening of Amine Chemistries used in the Synthesis of Hybrid Organic-Inorganic Perovskites

This paper describes a machine learning guided framework for screening the potential toxicity impact of amine chemistries used in the synthesis of hybrid organic-inorganic perovskites. Using a combination of a probabilistic molecular fingerprint technique that encodes bond connectivity (MinHash) coupled to non-linear data dimensionality reduction methods (UMAP), we develop an “Amine Atlas’. We show how the Amine Atlas can be used to rapidly screen the relative toxicity levels of amine molecules used in the synthesis of 2D and 3D perovskites and help identify safer alternatives. Our work also serves as a framework for rapidly identifying molecular similarity guided, structure-function relationships for safer materials chemistries that also incorporate sustainability/ toxicity concerns.

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.

Application of deep metric learning to molecular similarity

Graph based methods are increasingly important in chemistry and drug discovery, with applications ranging from QSAR to molecular generation. Combining graph neural networks and deep metric learning concepts, we expose a framework for quantifying molecular similarity based on learned embeddings separate from any endpoint. Using a minimal definition of similarity, and data from the ZINC database of public compounds, this work demonstrate the properties of the embedding and its suitability for a range of applications, among them a novel reconstruction loss method for training deep molecular auto-encoders. We also compare the performance of the embedding to standard practices, with a focus on known failure points and edge cases.