Molecular Informatics

The important role that the neurotrophin tyrosine kinase receptor - TrkB has in the pathogenesis of several neurodegenerative conditions such are Alzheimer's disease, Parkinson's disease, Huntington's disease, has been well described. This shouldn't be a surprise, since in the physiological conditions, once activated by brain-derived neurotrophic factor (BDNF) and neurotrophin-4/5 (NT-4/5), the TrkB receptor promotes neuronal survival, differentiation and synaptic function. Considering that the natural ligands for TrkB receptor are large proteins, it is a challenge to discover small molecule capable to mimic their effects...

Increasing antimicrobial resistance (AMR) represents a global healthcare threat. To decrease the spread of AMR and associated mortality, methods for rapid selection of optimal antibiotic treatment are urgently needed. Machine learning (ML) models based on genomic data to predict resistant phenotypes can serve as a fast screening tool prior to phenotypic testing. Nonetheless, many existing ML methods lack interpretability. Therefore, we present a methodology for visualization of sequence space and AMR prediction based on the non-linear dimensionality reduction method - generative topographic mapping (GTM)...

The application of various in-silico-based approaches for the prediction of various properties of materials has been an effective alternative to experimental methods. Recently, the concepts of Quantitative structure-property relationship (QSPR) and read-across (RA) methods were merged to develop a new emerging chemoinformatic tool: read-across structure-property relationship (RASPR). The RASPR method can be applicable to both large and small datasets as it uses various similarity and error-based measures. It has also been observed that RASPR models tend to have an increased external predictivity compared to the corresponding QSPR models...

When designing a machine learning-based scoring function, we access a limited number of protein-ligand complexes with experimentally determined binding affinity values, representing only a fraction of all possible protein-ligand complexes. Consequently, it is crucial to report a measure of confidence and quantify the uncertainty in the model's predictions during test time. Here, we adopt the conformal prediction technique to evaluate the confidence of a prediction for each member of the core set of the CASF 2016 benchmark...

In the past years the interest in Solute Carrier Transporters (SLC) has increased due to their potential as drug targets. At the same time, macrocycles demonstrated promising activities as therapeutic agents. However, the overall macrocycle/SLC-transporter interaction landscape has not been fully revealed yet. In this study, we present a statistical analysis of macrocycles with measured activity against SLC-transporter. Using a data mining pipeline based on KNIME retrieved in total 825 bioactivity data points of macrocycles interacting with SLC-transporter...

De novo design has been a hotly pursued topic for many years. Most recent developments have involved the use of deep learning methods for generative molecular design. Despite increasing levels of algorithmic sophistication, the design of molecules that are synthetically accessible remains a major challenge. Reaction-based de novo design takes a conceptually simpler approach and aims to address synthesisability directly by mimicking synthetic chemistry and driving structural transformations by known reactions that are applied in a stepwise manner...

Transporters play an indispensable role in facilitating the transport of nutrients, signaling molecules and the elimination of metabolites and toxins in human cells. Contemporary computational methods have been employed in the prediction of transporter inhibitors. However, these methods often focus on isolated endpoints, overlooking the interactions between transporters and lacking good interpretation. In this study, we integrated a comprehensive dataset and constructed models to assess the inhibitory effects on seven transporters...

Machine learning models have found numerous successful applications in computational drug discovery. A large body of these models represents molecules as sequences since molecular sequences are easily available, simple, and informative. The sequence-based models often segment molecular sequences into pieces called chemical words ,analogous to the words that make up sentences in human languages, and then apply advanced natural language processing techniques for tasks such as de novo drug design, property prediction, and binding affinity prediction...

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Peptides are potentially useful modalities of drugs; however, cell membrane permeability is an obstacle in peptide drug discovery. The identification of bioactive peptides for a therapeutic target is also challenging because of the huge amino acid sequence patterns of peptides. In this study, we propose a novel computational method, PEptide generation system using Neural network Trained on Amino acid sequence data and Gaussian process-based optimizatiON (PENTAGON), to automatically generate new peptides with desired bioactivity and cell membrane permeability...

[[1]](#ref-0001) Here, we investigate the reproducibility and modelability of kinetic solubility assays. We first analyzed the relationship between kinetic and thermodynamic solubility data, and then examined the consistency of data from different kinetic assays. In this contribution, we report differences between Kinetic aqueous or buffer solubility is important parameter measuring suitability of compounds for high throughput assays in early drug discovery while thermodynamic solubility is reserved for later stages of drug discovery and development...

Tuberculosis (TB) is the second leading cause of mortality after COVID-19, with a global death toll of 1.6 million in 2021. The escalating situation of drug-resistant forms of TB has threatened the current TB management strategies. New therapeutics with novel mechanisms of action are urgently required to address the current global TB crisis. The essential mycobacterial primase DnaG with no structural homology to homo sapiens presents itself as a good candidate for drug targeting. In the present study, Mitoxantrone and Vapreotide, two FDA-approved drugs, were identified as potential anti-mycobacterial agents...

Fungal infections caused by Candida are still a public health concern. Particularly, the resistance to traditional chemotherapeutic agents is a major issue that requires efforts to develop new therapies. One of the most interesting approaches to finding new active compounds is drug repurposing aided by computational methods. In this work, two databases containing anticandidal agents and drugs were studied employing cheminformatics and compared by similarity methods. The results showed 36 drugs with high similarities to some candicidals...

30.0 Rapid and accurate prediction of bandgaps and efficiency of perovskite solar cells is a crucial challenge for various solar cell applications. Existing theoretical and experimental methods often accurately measure these parameters; however, these methods are costly and time-consuming. Machine learning-based approaches offer a promising and computationally efficient method to address this problem. In this study, we trained different machine learning(ML) models using previously reported experimental data...

The availability of patent chemical data offers public access to a chemical space that is not well covered by other sources collecting small molecules from scholarly literature. However, open applications to facilitate the search and analysis of biologically-relevant molecular structures present in patents are still largely missing. We have developed CIPSI, an open Chemical Intellectual Property Service @ IMIM to assist medicinal chemists in searching and analysing molecules in SureChEMBL patents. The current version contains 6,240,500 molecules from 236,689 pharmacological patents, of which 5,949,214 are confidently assigned to core chemical structures reminiscent of the Markush structure in the patent claim...

In drug discovery, chemical language models (CLMs) originating from natural language processing offer new opportunities for molecular design. CLMs have been developed using recurrent neural network (RNN) or transformer architectures. For the predictive performance of RNN-based encoder-decoder frameworks and transformers, attention mechanisms play a central role. Among others, emerging application areas for CLMs include constrained generative modeling and the prediction of chemical reactions or drug-target interactions...

Numerous studies reported an association between GABA A R subunit genes and epilepsy, eating disorders, autism spectrum disorders, neurodevelopmental disorders, and bipolar disorders. This study was aimed to find some potential positive allosteric modulators and was performed by combining the in silico approach with further in vitro evaluation of its real activity. We started from the GABA A R-diazepam complexes and assembled a lipid embedded protein ensemble to refine it via molecular dynamics (MD) simulation...

tkinter GUIDEMOL is a Python computer program based on the RDKit software to process molecular structures and calculate molecular descriptors with a graphical user interface using the package. It can calculate descriptors already implemented in RDKit as well as grid representations of 3D molecular structures using the electrostatic potential or voxels. The GUIDEMOL app provides easy access to RDKit tools for chemoinformatics users with no programming skills and can be adapted to calculate other descriptors or to trigger other procedures...

Predicting the taste of molecules is of critical importance in the food and beverages, flavor, and pharmaceutical industries for the design and screening of new tastants. In this work, we have built deep learning models to classify sweet, bitter, and umami molecules- the three basic tastes whose sensation is mediated by G protein-coupled receptors. An extensive dataset containing 1466 bitter, 1764 sweet, and 238 umami tastants was curated from existing literature. We analyzed the chemical characteristics of the molecules, with special focus on the presence of different functional groups...

Agonists of the β2 adrenergic receptor (ADRB2) are an important class of medications used for the treatment of respiratory diseases. They can be classified as short acting (SABA) or long acting (LABA), with each class playing a different role in patient management. In this work we explored both ligand-based and structure-based high-throughput approaches to classify β2-agonists based on their duration of action. A completely in-silico prediction pipeline using an AlphaFold generated structure was used for structure-based modelling...