scholarly journals Custom ML Module of AIDrugApp for Molecular Identification, Descriptor Calculation, and Building ML/DL QSAR Models

2021 ◽  
Author(s):  
Divya Karade
Keyword(s):  
Molecular Identification ◽  
Chemical Space ◽  
Biological Properties ◽  
Free Access ◽  
The Novel ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Qsar Models ◽  
Efficient Exploration ◽  
Tools And Techniques

Computer-aided drug design (CADD) techniques continue to struggle to provide a useful advance in the area of drug development due to the difficulties in an efficient exploration of the vast drug-like chemical space to uncover new chemical compounds with desired biological properties. Other challenges that users must overcome in order to fully use the potential of CADD tools and techniques include a lack of completely autonomous methods, the necessity for retraining even after deployment, and their lack of interpretability. To solve this issue, we created the ‘Custom ML Tools’ integrated within the framework of ‘AIDrugAPP’. ‘Custom ML Tools’ includes four modules: ‘Mol Identifier’, ‘DesCal’, ‘AutoDL’, and ‘Auto-Multi-ML’ which give users free access to molecular identification using SMILES and compound names, similarity search, descriptor calculation, the building of ML/DL QSAR models, and their usage in predicting new data. The study demonstrates the potential of the novel tool for computational investigations in drug discovery research. The WebApp with its modules has therefore been made available for public use at: https://sars-covid-app.herokuapp.com/

Synthetic Strategies and Biological Potential of Coumarin-Chalcone Hybrids: A New Dimension to Drug Design

2020 ◽  
Vol 24 (4) ◽  
pp. 402-438
Author(s):  
Sharda Pasricha ◽  
Pragya Gahlot
Keyword(s):  
Drug Discovery ◽  
Chemical Potential ◽  
Wide Spectrum ◽  
Biological Properties ◽  
Sar Studies ◽  
Discovery Research ◽  
Biological Potential ◽  
Drug Discovery Research ◽  
Hybrid Molecules ◽  
Agent Development

Privileged scaffolds are ubiquitous as effective templates in drug discovery regime. Natural and synthetically derived hybrid molecules are one such attractive scaffold for therapeutic agent development due to their dual or multiple modes of action, minimum or no side effects, favourable pharmacokinetics and other advantages. Coumarins and chalcone are two important classes of natural products affording diverse pharmacological activities which make them ideal templates for building coumarin-chalcone hybrids as effective biological scaffold for drug discovery research. Provoked by the promising medicinal application of hybrid molecules as well as those of coumarins and chalcones, the medicinal chemists have used molecular hybridisation strategy to report dozens of coumarin- chalcone hybrids with a wide spectrum of biological properties including anticancer, antimicrobial, antimalarial, antioxidant, anti-tubercular and so on. The present review provides a systematic summary on synthetic strategies, biological or chemical potential, SAR studies, some mechanisms of action and some plausible molecular targets of synthetic coumarin-chalcone hybrids published from 2001 till date. The review is expected to assist medicinal chemists in the effective and successful development of coumarin- chalcone hybrid based drug discovery regime.

scholarly journals Saponins from Quillaja saponaria and Quillaja brasiliensis: Particular Chemical Characteristics and Biological Activities

Molecules ◽  
2019 ◽  
Vol 24 (1) ◽  
pp. 171 ◽  
Author(s):  
Juliane Deise Fleck ◽  
Andresa Heemann Betti ◽  
Francini Pereira Da Silva ◽  
Eduardo Artur Troian ◽  
Cristina Olivaro ◽  
...  
Keyword(s):  
Vaccine Development ◽  
Cytotoxic T Lymphocyte ◽  
Biological Activities ◽  
Biological Properties ◽  
Industrial Applications ◽  
Chemical Characteristics ◽  
Antigen Uptake ◽  
Quillaja Saponaria ◽  
Discovery Research ◽  
Drug Discovery Research

Quillaja saponaria Molina represents the main source of saponins for industrial applications. Q. saponaria triterpenoids have been studied for more than four decades and their relevance is due to their biological activities, especially as a vaccine adjuvant and immunostimulant, which have led to important research in the field of vaccine development. These saponins, alone or incorporated into immunostimulating complexes (ISCOMs), are able to modulate immunity by increasing antigen uptake, stimulating cytotoxic T lymphocyte production (Th1) and cytokines (Th2) in response to different antigens. Furthermore, antiviral, antifungal, antibacterial, antiparasitic, and antitumor activities are also reported as important biological properties of Quillaja triterpenoids. Recently, other saponins from Q. brasiliensis (A. St.-Hill. & Tul.) Mart. were successfully tested and showed similar chemical and biological properties to those of Q. saponaria barks. The aim of this manuscript is to summarize the current advances in phytochemical and pharmacological knowledge of saponins from Quillaja plants, including the particular chemical characteristics of these triterpenoids. The potential applications of Quillaja saponins to stimulate further drug discovery research will be provided.

scholarly journals DrugSpaceX: a large screenable and synthetically tractable database extending drug space

2020 ◽  
Vol 49 (D1) ◽  
pp. D1170-D1178
Author(s):  
Tianbiao Yang ◽  
Zhaojun Li ◽  
Yingjia Chen ◽  
Dan Feng ◽  
Guangchao Wang ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Chemical Space ◽  
Three Dimensional ◽  
Chemical Properties ◽  
Biological Properties ◽  
Chemical Structures ◽  
Drug Molecules ◽  
Ligand Identification ◽  
Strategy Planning ◽  
Efficient Exploration

Abstract One of the most prominent topics in drug discovery is efficient exploration of the vast drug-like chemical space to find synthesizable and novel chemical structures with desired biological properties. To address this challenge, we created the DrugSpaceX (https://drugspacex.simm.ac.cn/) database based on expert-defined transformations of approved drug molecules. The current version of DrugSpaceX contains >100 million transformed chemical products for virtual screening, with outstanding characteristics in terms of structural novelty, diversity and large three-dimensional chemical space coverage. To illustrate its practical application in drug discovery, we used a case study of discoidin domain receptor 1 (DDR1), a kinase target implicated in fibrosis and other diseases, to show DrugSpaceX performing a quick search of initial hit compounds. Additionally, for ligand identification and optimization purposes, DrugSpaceX also provides several subsets for download, including a 10% diversity subset, an extended drug-like subset, a drug-like subset, a lead-like subset, and a fragment-like subset. In addition to chemical properties and transformation instructions, DrugSpaceX can locate the position of transformation, which will enable medicinal chemists to easily integrate strategy planning and protection design.

Total Synthesis of Axially-Chiral Cannabinols: A New Platform for Cannabinoid-Based Drug Discovery

2019 ◽  
Author(s):  
Primali Navaratne ◽  
Jenny Wilkerson ◽  
Kavindri Ranasinghe ◽  
Evgeniya Semenova ◽  
Lance McMahon ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Total Synthesis ◽  
Ground State ◽  
Chemical Space ◽  
Modern Medicine ◽  
The Novel ◽  
Pharmaceutical Development ◽  
Bioactive Component ◽  
Axially Chiral ◽  
New Directions

<div> <div> <div> <p>Phytocannabinoids, molecules isolated from cannabis, are gaining attention as promising leads in modern medicine, including pain management. Considering the urgent need for combating the opioid crisis, new directions for the design of cannabinoid-inspired analgesics are of immediate interest. In this regard, we have hypothesized that axially-chiral-cannabinols (ax-CBNs), unnatural (and unknown) isomers of cannabinol (CBN) may be valuable scaffolds for cannabinoid-inspired drug discovery. There are multiple reasons for thinking this: (a) ax-CBNs would have ground-state three-dimensionality akin to THC, a key bioactive component of cannabis, (b) ax-CBNs at their core structure are biaryl molecules, generally attractive platforms for pharmaceutical development due to their ease of functionalization and stability, and (c) atropisomerism with respect to phytocannabinoids is unexplored “chemical space.” Herein we report a scalable total synthesis of ax-CBNs, examine physical properties experimentally and computationally, and provide preliminary behavioral and analgesic analysis of the novel scaffolds. </p> </div> </div> </div>

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

scholarly journals iPSC technology-based regenerative medicine for kidney diseases

2021 ◽  
Author(s):  
Kenji Osafune
Keyword(s):  
Regenerative Medicine ◽  
Cell Therapy ◽  
Kidney Development ◽  
Disease Modeling ◽  
Kidney Diseases ◽  
Collecting Duct ◽  
Current Status ◽  
Renal Tubules ◽  
Discovery Research ◽  
Drug Discovery Research

AbstractWith few curative treatments for kidney diseases, increasing attention has been paid to regenerative medicine as a new therapeutic option. Recent progress in kidney regeneration using human-induced pluripotent stem cells (hiPSCs) is noteworthy. Based on the knowledge of kidney development, the directed differentiation of hiPSCs into two embryonic kidney progenitors, nephron progenitor cells (NPCs) and ureteric bud (UB), has been established, enabling the generation of nephron and collecting duct organoids. Furthermore, human kidney tissues can be generated from these hiPSC-derived progenitors, in which NPC-derived glomeruli and renal tubules and UB-derived collecting ducts are interconnected. The induced kidney tissues are further vascularized when transplanted into immunodeficient mice. In addition to the kidney reconstruction for use in transplantation, it has been demonstrated that cell therapy using hiPSC-derived NPCs ameliorates acute kidney injury (AKI) in mice. Disease modeling and drug discovery research using disease-specific hiPSCs has also been vigorously conducted for intractable kidney disorders, such as autosomal dominant polycystic kidney disease (ADPKD). In an attempt to address the complications associated with kidney diseases, hiPSC-derived erythropoietin (EPO)-producing cells were successfully generated to discover drugs and develop cell therapy for renal anemia. This review summarizes the current status and future perspectives of developmental biology of kidney and iPSC technology-based regenerative medicine for kidney diseases.

scholarly journals Diversity oriented Deep Reinforcement Learning for targeted molecule generation

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Tiago Pereira ◽  
Maryam Abbasi ◽  
Bernardete Ribeiro ◽  
Joel P. Arrais
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Reinforcement Learning ◽  
Deep Neural Networks ◽  
Chemical Space ◽  
Biological Properties ◽  
Training Process ◽  
Training Strategy ◽  
Inhibitory Power ◽  
Exploratory Strategy

AbstractIn this work, we explore the potential of deep learning to streamline the process of identifying new potential drugs through the computational generation of molecules with interesting biological properties. Two deep neural networks compose our targeted generation framework: the Generator, which is trained to learn the building rules of valid molecules employing SMILES strings notation, and the Predictor which evaluates the newly generated compounds by predicting their affinity for the desired target. Then, the Generator is optimized through Reinforcement Learning to produce molecules with bespoken properties. The innovation of this approach is the exploratory strategy applied during the reinforcement training process that seeks to add novelty to the generated compounds. This training strategy employs two Generators interchangeably to sample new SMILES: the initially trained model that will remain fixed and a copy of the previous one that will be updated during the training to uncover the most promising molecules. The evolution of the reward assigned by the Predictor determines how often each one is employed to select the next token of the molecule. This strategy establishes a compromise between the need to acquire more information about the chemical space and the need to sample new molecules, with the experience gained so far. To demonstrate the effectiveness of the method, the Generator is trained to design molecules with an optimized coefficient of partition and also high inhibitory power against the Adenosine $$A_{2A}$$ A 2 A and $$\kappa$$ κ opioid receptors. The results reveal that the model can effectively adjust the newly generated molecules towards the wanted direction. More importantly, it was possible to find promising sets of unique and diverse molecules, which was the main purpose of the newly implemented strategy.

scholarly journals Development of a Selection Method for Discovering Irreversible (Covalent) Binders from a DNA-Encoded Library

2018 ◽  
Vol 24 (2) ◽  
pp. 169-174 ◽  
Author(s):  
Zhengrong Zhu ◽  
LaShadric C. Grady ◽  
Yun Ding ◽  
Kenneth E. Lind ◽  
Christopher P. Davie ◽  
...  
Keyword(s):  
Magnetic Beads ◽  
Chemical Probes ◽  
Lead Discovery ◽  
3C Protease ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Affinity Resin ◽  
Covalent Ligands ◽  
Developing Method ◽  
Unique Mechanism

DNA-encoded libraries (DELs) have been broadly applied to identify chemical probes for target validation and lead discovery. To date, the main application of the DEL platform has been the identification of reversible ligands using multiple rounds of affinity selection. Irreversible (covalent) inhibition offers a unique mechanism of action for drug discovery research. In this study, we report a developing method of identifying irreversible (covalent) ligands from DELs. The new method was validated by using 3C protease (3CP) and on-DNA irreversible tool compounds (rupintrivir derivatives) spiked into a library at the same concentration as individual members of that library. After affinity selections against 3CP, the irreversible tool compounds were specifically enriched compared with the library members. In addition, we compared two immobilization methods and concluded that microscale columns packed with the appropriate affinity resin gave higher tool compound recovery than magnetic beads.

scholarly journals Modern Approaches in the Discovery and Development of Plant-Based Natural Products and Their Analogues as Potential Therapeutic Agents

Molecules ◽  
2022 ◽  
Vol 27 (2) ◽  
pp. 349
Author(s):  
Asim Najmi ◽  
Sadique A. Javed ◽  
Mohammed Al Bratty ◽  
Hassan A. Alhazmi
Keyword(s):  
Natural Products ◽  
Drug Discovery ◽  
Natural Product ◽  
Therapeutic Agents ◽  
Throughput Capacity ◽  
Scientific Interest ◽  
Comprehensive Overview ◽  
Natural Sources ◽  
Discovery Research ◽  
Drug Discovery Research

Natural products represents an important source of new lead compounds in drug discovery research. Several drugs currently used as therapeutic agents have been developed from natural sources; plant sources are specifically important. In the past few decades, pharmaceutical companies demonstrated insignificant attention towards natural product drug discovery, mainly due to its intrinsic complexity. Recently, technological advancements greatly helped to address the challenges and resulted in the revived scientific interest in drug discovery from natural sources. This review provides a comprehensive overview of various approaches used in the selection, authentication, extraction/isolation, biological screening, and analogue development through the application of modern drug-development principles of plant-based natural products. Main focus is given to the bioactivity-guided fractionation approach along with associated challenges and major advancements. A brief outline of historical development in natural product drug discovery and a snapshot of the prominent natural drugs developed in the last few decades are also presented. The researcher’s opinions indicated that an integrated interdisciplinary approach utilizing technological advances is necessary for the successful development of natural products. These involve the application of efficient selection method, well-designed extraction/isolation procedure, advanced structure elucidation techniques, and bioassays with a high-throughput capacity to establish druggability and patentability of phyto-compounds. A number of modern approaches including molecular modeling, virtual screening, natural product library, and database mining are being used for improving natural product drug discovery research. Renewed scientific interest and recent research trends in natural product drug discovery clearly indicated that natural products will play important role in the future development of new therapeutic drugs and it is also anticipated that efficient application of new approaches will further improve the drug discovery campaign.

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