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Journal articles on the topic 'Molecule discovery'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Shamima Shultana, Kazi M Maraz, Arwah Ahmed, Tanzila Sultana, and Ruhul A Khan. "Drug design, discovery and development and their safety or efficacy on human body." GSC Biological and Pharmaceutical Sciences 17, no. 2 (2021): 113–22. http://dx.doi.org/10.30574/gscbps.2021.17.2.0330.

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Drug Design, often mentioned as rational drug design or just rational design. It is defined as the study of the shape of molecules in order to determine how they will bind receptors on cells or combine with other molecules. It is based on molecular shape or architecture is an alternative to blindly testing hundreds of molecules to see if one or more of them will bind cellular or molecular targets. The drug is an organic molecule, when it is bind to target site it can either inhibit or activate the function of a bio-molecule which results in therapeutic benefit.

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Shamima, Shultana, M. Maraz Kazi, Ahmed Arwah, Sultana Tanzila, and A. Khan Ruhul. "Drug design, discovery and development and their safety or efficacy on human body." GSC Biological and Pharmaceutical Sciences 17, no. 2 (2021): 113–22. https://doi.org/10.5281/zenodo.5763084.

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Drug Design, often mentioned as rational drug design or just rational design. It is defined as the study of the shape of molecules in order to determine how they will bind receptors on cells or combine with other molecules. It is based on molecular shape or architecture is an alternative to blindly testing hundreds of molecules to see if one or more of them will bind cellular or molecular targets. The drug is an organic molecule, when it is bind to target site it can either inhibit or activate the function of a bio-molecule which results in therapeutic benefit.

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Obi, E. D., J. A. Yentumi, D. Mbatuegwu, O. I. Omotuyi, O. O. Ajayi, and A. Nwokoro. "LAIgnd: Revolutionizing Drug Discovery with Advanced AI-Driven Molecule Generation." Advances in Multidisciplinary & Scientific Research Journal Publication 15, no. 4 (2024): 1–10. http://dx.doi.org/10.22624/aims/cisdi/v15n3p4.

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De novo molecular generation is crucial for advancing drug discovery and chemical research. This accelerates the search for new drug candidates and deepens our understanding of molecular diversity. The development of deep learning has propelled and expedited the de novo molecular generation. Generative networks, particularly Variational Autoencoders (VAEs), can randomly produce new molecules and modify molecular structures to enhance specific chemical properties, which are essential for advancing drug discovery. Although VAEs offer numerous advantages, they are hindered by limitations that aff

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Alldritt, Benjamin, Prokop Hapala, Niko Oinonen, et al. "Automated structure discovery in atomic force microscopy." Science Advances 6, no. 9 (2020): eaay6913. http://dx.doi.org/10.1126/sciadv.aay6913.

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Atomic force microscopy (AFM) with molecule-functionalized tips has emerged as the primary experimental technique for probing the atomic structure of organic molecules on surfaces. Most experiments have been limited to nearly planar aromatic molecules due to difficulties with interpretation of highly distorted AFM images originating from nonplanar molecules. Here, we develop a deep learning infrastructure that matches a set of AFM images with a unique descriptor characterizing the molecular configuration, allowing us to predict the molecular structure directly. We apply this methodology to res

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Vittaladevaram, Viswanath, and Kranthi Kuruti. "Application of Cryo-Electron Microscopy on Drug Discovery." Microscopy and Microanalysis 27, S1 (2021): 3250. http://dx.doi.org/10.1017/s143192762101120x.

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AbstractThe key aspect for development of novel drug molecules is to perform structural determination of target molecule associated with its ligand. One such tool that provides insights towards structure of molecule is Cryo-electron microscopy which covers biological targets that are intractable. Examination of proteins can be carried out in native state, as the samples are frozen at -175 degree Celsius i.e. cryogenic temperatures. In addition to this, there were no limits for molecular and functional structures of proteins that can be imagined in 3-dimensional form. This includes ligands whic

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Chen, Shi-Jie, and Yuanzhe Zhou. "Harnessing Computational Approaches for RNA-Targeted Drug Discovery." RNA NanoMed 1, no. 1 (2024): 1–15. https://doi.org/10.59566/isrnn.2024.0101001.

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RNA molecules have emerged as promising therapeutic targets due to their diverse functional and regulatory roles within cells. Computational modeling in RNA-targeted drug discovery presents a significant opportunity to expedite the discovery of novel small molecule compounds. However, this field encounters unique challenges compared to protein-targeted drug design, primarily due to limited experimental data availability and current models’ inability to adequately address RNA’s conformational flexibility during ligand recognition. Despite these challenges, several studies have successfully iden

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Jain, Akash, Ilya A. Shkrob, and Rajeev S. Assary. "Synthesis-Driven Computational Discovery of Organic Redoxmers for Non-Aqueous Redox Flow Batteries." ECS Meeting Abstracts MA2023-01, no. 3 (2023): 724. http://dx.doi.org/10.1149/ma2023-013724mtgabs.

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Organic non-aqueous redox flow batteries (RFBs) are promising grid-scale energy storage systems for storing intermittent renewable energy in molecules. For practical applications, low-cost and stable redox active molecules (redoxmers) that display a large redox potential window and long electrochemical cycling stability are required to deliver a high-energy-density RFB with a long battery cycle life. To accelerate the discovery of redoxmer molecules, previous studies have utilized machine learning (ML) methods along with first-principles simulations. Although many ML-suggested molecules show p

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Charlop-Powers, Zachary, Aleksandr Milshteyn, and Sean F. Brady. "Metagenomic small molecule discovery methods." Current Opinion in Microbiology 19 (June 2014): 70–75. http://dx.doi.org/10.1016/j.mib.2014.05.021.

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MacRae, Calum A., and Randall T. Peterson. "Zebrafish-Based Small Molecule Discovery." Chemistry & Biology 10, no. 10 (2003): 901–8. http://dx.doi.org/10.1016/j.chembiol.2003.10.003.

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10

Steinhagen, Henning. "Igniting Small-Molecule Drug Discovery." ChemMedChem 11, no. 2 (2016): 148–49. http://dx.doi.org/10.1002/cmdc.201500580.

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11

Zou, Mingjie, Haiyuan Zhou, Letian Gu, Jingzi Zhang, and Lei Fang. "Therapeutic Target Identification and Drug Discovery Driven by Chemical Proteomics." Biology 13, no. 8 (2024): 555. http://dx.doi.org/10.3390/biology13080555.

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Throughout the human lifespan, from conception to the end of life, small molecules have an intrinsic relationship with numerous physiological processes. The investigation into small-molecule targets holds significant implications for pharmacological discovery. The determination of the action sites of small molecules provide clarity into the pharmacodynamics and toxicological mechanisms of small-molecule drugs, assisting in the elucidation of drug off-target effects and resistance mechanisms. Consequently, innovative methods to study small-molecule targets have proliferated in recent years, wit

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Li, Qingxin, and CongBao Kang. "Mechanisms of Action for Small Molecules Revealed by Structural Biology in Drug Discovery." International Journal of Molecular Sciences 21, no. 15 (2020): 5262. http://dx.doi.org/10.3390/ijms21155262.

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Small-molecule drugs are organic compounds affecting molecular pathways by targeting important proteins. These compounds have a low molecular weight, making them penetrate cells easily. Small-molecule drugs can be developed from leads derived from rational drug design or isolated from natural resources. A target-based drug discovery project usually includes target identification, target validation, hit identification, hit to lead and lead optimization. Understanding molecular interactions between small molecules and their targets is critical in drug discovery. Although many biophysical and bio

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Shubhangi, H. Bhowate* Dr. Dinesh R. Chaple Dr. Alpana J. asnani Pranita I. Rathod Aishwarya V. Lichade Vaishnavi S. Bhure. "Molecular Docking: A Powerful Tool In Modern Drug Discovery And Its Approaches." International Journal in Pharmaceutical Sciences 1, no. 10 (2023): 170–81. https://doi.org/10.5281/zenodo.10017630.

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The field of computer-aided drug design and discovery (CADD) has been growing rapidly in recent years, with many successes. Both large pharmaceutical companies and academia use CADD for drug lead discovery. Advances in structural informatics, genomics, and proteomics have been vital in modern drug discovery and development. Research over the past two decades has focused on studying different docking algorithms to predict the active site of a molecule. Various docking programs have been developed to visualize the 3D structure of a molecule, and docking scores can be analysed using different com

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Namitha K N and V Velmurugan. "Review of bioinformatic tools used in Computer Aided Drug Design (CADD)." World Journal of Advanced Research and Reviews 14, no. 2 (2022): 453–65. http://dx.doi.org/10.30574/wjarr.2022.14.2.0394.

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Drug discovery is а time consuming рrосess of finding out a new drug molecule. The process takes many years to complete and needs human resource. These is difficulties have been overcome by introducing computer programmes in drug discovery (CADD) which includes target identification, hit identification, and molecular modification of а lead compound to optimize desired effects and minimize side effects, based on the knowledge of their biological targets. Molecular modelling is the process of designing a molecule with a computer-based collection of programmes (in-silico design) for deriving, rep

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Namitha, K. N., and Velmurugan V. "Review of bioinformatic tools used in Computer Aided Drug Design (CADD)." World Journal of Advanced Research and Reviews 14, no. 2 (2022): 453–65. https://doi.org/10.5281/zenodo.7298898.

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Drug discovery is а time consuming рrосess of finding out a new drug molecule. The process takes many years to complete and needs human resource. These is difficulties have been overcome by introducing computer programmes in drug discovery (CADD) which includes target identification, hit identification, and molecular modification of а lead compound to optimize desired effects and minimize side effects, based on the knowledge of their biological targets. Molecular modelling is the process of designing a molecule with a computer-based collection of programmes (in-silico design) for deriving, rep

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16

Ruiz Puentes, Paola, Natalia Valderrama, Cristina González, et al. "PharmaNet: Pharmaceutical discovery with deep recurrent neural networks." PLOS ONE 16, no. 4 (2021): e0241728. http://dx.doi.org/10.1371/journal.pone.0241728.

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The discovery and development of novel pharmaceuticals is an area of active research mainly due to the large investments required and long payback times. As of 2016, the development of a novel drug candidate required up to $ USD 2.6 billion in investment for only 10% rate of approval by the FDA. To help decreasing the costs associated with the process, a number of in silico approaches have been developed with relatively low success due to limited predicting performance. Here, we introduced a machine learning-based algorithm as an alternative for a more accurate search of new pharmacological ca

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17

KIRBOĞA, Kevser Kübra, and Ecir KÜÇÜKSİLLE. "Bilgisayar Destekli İlaç Keşfi Üzerine Bakışlar." Dicle Üniversitesi Fen Bilimleri Enstitüsü Dergisi 11, no. 2 (2022): 1. http://dx.doi.org/10.55007/dufed.1103457.

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The drug development and discovery process are challenging, take 15 to 20 years, and require approximately 1.5-2 billion dollars, from the critical selection of the target molecule to post-clinical market application. Several computational drug design methods identify and optimize target biologically lead compounds. Given the complexity and cost of the drug discovery process in recent years, computer-assisted drug discovery (CADD) has spread over a broad spectrum. CADD methods support the discovery of target molecules, optimization of small target molecules, analysis, and development processes

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Enisoglu Atalay, Vildan, and Semse Asar. "Determination of the inhibition effect of hesperetin and its derivatives on Candida glabrata by molecular docking method." European Chemistry and Biotechnology Journal, no. 1 (January 2, 2024): 27–38. http://dx.doi.org/10.62063/ecb-15.

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In the study, it was aimed to develop new candidate inhibitor molecules by targeting the AWP1 protein structure of Candida glabrata organism. Hesperetin molecule was taken as a reference and different substituted groups were attached to the determined ends of the molecule to increase the inhibition potential on the protein structure. A total of 100 molecules were designed and after conformer distribution using the Molecular Mechanics/MMFF method for each designed molecule, the area, volume, weight, energy, EHOMO, ELUMO, polarizability, dipole moment, log P values of these molecules were calcul

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Morgan, Gareth J. "Barriers to Small Molecule Drug Discovery for Systemic Amyloidosis." Molecules 26, no. 12 (2021): 3571. http://dx.doi.org/10.3390/molecules26123571.

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Inhibition of amyloid fibril formation could benefit patients with systemic amyloidosis. In this group of diseases, deposition of amyloid fibrils derived from normally soluble proteins leads to progressive tissue damage and organ failure. Amyloid formation is a complex process, where several individual steps could be targeted. Several small molecules have been proposed as inhibitors of amyloid formation. However, the exact mechanism of action for a molecule is often not known, which impedes medicinal chemistry efforts to develop more potent molecules. Furthermore, commonly used assays are pron

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Gu, Yaowen, Jiao Li, Hongyu Kang, Bowen Zhang, and Si Zheng. "Employing Molecular Conformations for Ligand-Based Virtual Screening with Equivariant Graph Neural Network and Deep Multiple Instance Learning." Molecules 28, no. 16 (2023): 5982. http://dx.doi.org/10.3390/molecules28165982.

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Ligand-based virtual screening (LBVS) is a promising approach for rapid and low-cost screening of potentially bioactive molecules in the early stage of drug discovery. Compared with traditional similarity-based machine learning methods, deep learning frameworks for LBVS can more effectively extract high-order molecule structure representations from molecular fingerprints or structures. However, the 3D conformation of a molecule largely influences its bioactivity and physical properties, and has rarely been considered in previous deep learning-based LBVS methods. Moreover, the relative bioactiv

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21

Ōsawa, Eiji. "Looking Back the Most Beautiful Molecule C60 after Quarter Century of Discovery." Visnik Nacional'noi' akademii' nauk Ukrai'ni, no. 09 (September 25, 2012): 27–35. http://dx.doi.org/10.15407/visn2012.09.030.

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22

Fessard, Thomas C., Kristina Goncharenko, Quentin Lefebvre, and Christophe Salomé. "Pushing the Frontiers of Accessible Chemical Space to Unleash Design Creativity and Accelerate Drug Discovery." CHIMIA International Journal for Chemistry 74, no. 10 (2020): 803–7. http://dx.doi.org/10.2533/chimia.2020.803.

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In highly competitive research environments, the ability to access more complex structural spaces efficiently is a predictor of a company's ability to generate novel IP-protected small molecule candidates with adequate properties, hence filling their development pipelines. SpiroChem is consistently developing new synthetic methodologies and strategies to access complex molecular structure, thereby facilitating and accelerating small molecule drug discovery. Pushing the limits of what are perceived as complex molecular structures allows SpiroChem and its clients to unleash creativity and explor

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Gaba, Sonam, Salma Jamal, Open Source Drug Discovery Consortium, and Vinod Scaria. "Cheminformatics Models for Inhibitors ofSchistosoma mansoniThioredoxin Glutathione Reductase." Scientific World Journal 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/957107.

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Schistosomiasis is a neglected tropical disease caused by a parasiteSchistosoma mansoniand affects over 200 million annually. There is an urgent need to discover novel therapeutic options to control the disease with the recent emergence of drug resistance. The multifunctional protein, thioredoxin glutathione reductase (TGR), an essential enzyme for the survival of the pathogen in the redox environment has been actively explored as a potential drug target. The recent availability of small-molecule screening datasets against this target provides a unique opportunity to learn molecular properties

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Chen, Jie. "Pharmacochemical Strategies and Advances in Alzheimer's Disease Drug Development." Highlights in Science, Engineering and Technology 139 (April 28, 2025): 283–93. https://doi.org/10.54097/qbjryc26.

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Alzheimer's disease (AD) is one of the diseases that seriously threaten human life and health. Currently, the exact pathological mechanism of the disease is still unknown, and there are no drugs that can terminate or reverse the pathological process, which have limited the discovery of AD-related targets and the development of drugs that can effectively treat AD. However, with the deepening research on the molecular mechanism of the disease and the development of computers and artificial intelligence, some potential targets of AD have been discovered and their structures have been analyzed, an

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Whitehurst, Charles E., Naim Nazef, D. Allen Annis, et al. "Discovery and Characterization of Orthosteric and Allosteric Muscarinic M2 Acetylcholine Receptor Ligands by Affinity Selection-Mass Spectrometry." Journal of Biomolecular Screening 11, no. 2 (2005): 194–207. http://dx.doi.org/10.1177/1087057105284340.

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Screening assays using target-based affinity selection coupled with high-sensitivity detection technologies to identify small-molecule hits from chemical libraries can provide a useful discovery approach that complements traditional assay systems. Affinity selection-mass spectrometry (AS-MS) is one such methodology that holds promise for providing selective and sensitive high-throughput screening platforms. Although AS-MS screening platforms have been used to discover small-molecule ligands of proteins from many target families, they have not yet been used routinely to screen integral membrane

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Liu, Haoran, Youzhi Luo, Tianxiao Li, James Caverlee, and Martin Renqiang Min. "Learning Disentangled Equivariant Representation for Explicitly Controllable 3D Molecule Generation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 18 (2025): 18816–24. https://doi.org/10.1609/aaai.v39i18.34071.

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We consider the conditional generation of 3D drug-like molecules with explicit control over molecular properties such as drug-like properties (e.g., Quantitative Estimate of Druglikeness or Synthetic Accessibility score) and effectively binding to specific protein sites. To tackle this problem, we propose an E(3)-equivariant Wasserstein autoencoder and factorize the latent space of our generative model into two disentangled aspects: molecular properties and the remaining structural context of 3D molecules. Our model ensures explicit control over these molecular attributes while maintaining equ

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Bian, Yuemin, and Xiang-Qun Xie. "Artificial Intelligent Deep Learning Molecular Generative Modeling of Scaffold-Focused and Cannabinoid CB2 Target-Specific Small-Molecule Sublibraries." Cells 11, no. 5 (2022): 915. http://dx.doi.org/10.3390/cells11050915.

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Design and generation of high-quality target- and scaffold-specific small molecules is an important strategy for the discovery of unique and potent bioactive drug molecules. To achieve this goal, authors have developed the deep-learning molecule generation model (DeepMGM) and applied it for the de novo molecular generation of scaffold-focused small-molecule libraries. In this study, a recurrent neural network (RNN) using long short-term memory (LSTM) units was trained with drug-like molecules to result in a general model (g-DeepMGM). Sampling practices on indole and purine scaffolds illustrate

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Futaki, Shiroh. "Middle Molecule Drug Discovery and DDS." Drug Delivery System 35, no. 3 (2020): 167. http://dx.doi.org/10.2745/dds.35.167.

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29

Lawson, Alastair D. G. "Antibody-enabled small-molecule drug discovery." Nature Reviews Drug Discovery 11, no. 7 (2012): 519–25. http://dx.doi.org/10.1038/nrd3756.

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Skinner, Gary M., and Koen Visscher. "Single-Molecule Techniques for Drug Discovery." ASSAY and Drug Development Technologies 2, no. 4 (2004): 397–406. http://dx.doi.org/10.1089/adt.2004.2.397.

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Abou-Gharbia, Magid. "Discovery of Innovative Small Molecule Therapeutics." Journal of Medicinal Chemistry 52, no. 1 (2009): 2–9. http://dx.doi.org/10.1021/jm8012823.

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McKenna, Neil. "New Approaches Redefine Small Molecule Discovery." Genetic Engineering & Biotechnology News 32, no. 12 (2012): 22–23. http://dx.doi.org/10.1089/gen.32.12.08.

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Ohnmacht, Stephan A., and Stephen Neidle. "Small-molecule quadruplex-targeted drug discovery." Bioorganic & Medicinal Chemistry Letters 24, no. 12 (2014): 2602–12. http://dx.doi.org/10.1016/j.bmcl.2014.04.029.

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Stegmaier, K. "Genomic approaches to small molecule discovery." Leukemia 23, no. 7 (2009): 1226–35. http://dx.doi.org/10.1038/leu.2009.29.

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He, Xuezhong G., Guillermo Gerona-Navarro, and Samie R. Jaffrey. "Ligand Discovery Using Small Molecule Microarrays." Journal of Pharmacology and Experimental Therapeutics 313, no. 1 (2004): 1–7. http://dx.doi.org/10.1124/jpet.104.076943.

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Zhan, Weiqiang, Zhongxing Liang, Aizhi Zhu, et al. "Discovery of Small Molecule CXCR4 Antagonists." Journal of Medicinal Chemistry 50, no. 23 (2007): 5655–64. http://dx.doi.org/10.1021/jm070679i.

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Reidenbach, Andrew G., Michael F. Mesleh, Dominick Casalena, et al. "Multimodal small-molecule screening for human prion protein binders." Journal of Biological Chemistry 295, no. 39 (2020): 13516–31. http://dx.doi.org/10.1074/jbc.ra120.014905.

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Prion disease is a rapidly progressive neurodegenerative disorder caused by misfolding and aggregation of the prion protein (PrP), and there are currently no therapeutic options. PrP ligands could theoretically antagonize prion formation by protecting the native protein from misfolding or by targeting it for degradation, but no validated small-molecule binders have been discovered to date. We deployed a variety of screening methods in an effort to discover binders of PrP, including 19F-observed and saturation transfer difference (STD) NMR spectroscopy, differential scanning fluorimetry (DSF),

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Kroto, H. W., J. P. Hare, A. Sarkar, K. Hsu, M. Terrones, and J. R. Abeysinghe. "New Horizons in Carbon Chemistry and Materials Science." MRS Bulletin 19, no. 11 (1994): 51–55. http://dx.doi.org/10.1557/s0883769400048417.

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The discovery that C60 Buckminsterfullerene may be created spontaneously in high yield when carbon vapor condenses indicates that graphene sheet curvature and closure is a common occurrence during carbon nucleation to form extended networks. As a consequence, a net microscopic perspective on graphitelike carbonaceous materials has evolved. This perspective is summarized here because the net observations relate to various types of nonplanar graphitic structures that promise to be useful as viable nanoscale engineering materials.C60 Buckminsterfullerene was discovered in 1985 among the products

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Dhaval, V. Patel Mukesh Nandave Prashant S. Kharkar. "PHARMACOPHORE MODELLING FOR THE DISCOVERY OF SYSTEM XC- ANTIPORTER INHIBITORS." INDO AMERICAN JOURNAL OF PHARMACEUTICAL RESEARCH 07, no. 09 (2017): 532–36. https://doi.org/10.5281/zenodo.1036492.

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Cancer is one of the major disorders with increasing rates of morbidity and mortality. Recent drug discovery of anti cancer drug has identified several molecular targets and tried to achieve a goal of therapeutic effecative and safe molecule. Amongst these, system xc- antiporter is a novel promising target to control cancer progression. This antiporter is found to be over expressed in majority of cancer cells and functions by transporting amino acids, cystine and glutamate, in opposite directions. System xc- antiporter uptakes one molecule of cystine with the release of one molecule of glutama

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Zubair, Tanzida, and Debasish Bandyopadhyay. "Small Molecule EGFR Inhibitors as Anti-Cancer Agents: Discovery, Mechanisms of Action, and Opportunities." International Journal of Molecular Sciences 24, no. 3 (2023): 2651. http://dx.doi.org/10.3390/ijms24032651.

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Epidermal growth factor receptors (EGFRs) are a class of receptor tyrosine kinase that are also called ErbB1 and HER1. EGFR tyrosine kinase activity inhibition is considered a promising therapeutic strategy for the treatment of cancer. Many small-molecule inhibitors of EGFR tyrosine kinase (EGFR-TK), from medicinally privileged molecules to commercial drugs, have been overviewed. Particular attention has been paid to the structure of the molecule and its mechanism of action if reported. Subsequent classification of the molecules under discussion has been carried out. Both natural and synthetic

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Maslehat, Sholeh, Soroush Sardari, and Mahboube Ganji Arjenaki. "Frequency and Importance of Six Functional Groups that Play a Role in Drug Discovery." Biosciences, Biotechnology Research Asia 15, no. 3 (2018): 541–48. http://dx.doi.org/10.13005/bbra/2659.

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Small molecules are composed of chemical functional groups; they are sets of connected atoms or atom groups that determine properties and reactivity of the parent molecule. DrugBank is a rich source of information that containing molecular data about small molecules, their mechanisms, pharmaceutical interaction and targets. In this study, After collecting data of small drug molecules from DrugBank database and classifying them in different categories based on their mechanism of action, the therapeutic properties of the molecules were recorded. Finally, the functional group from the pharmaceuti

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Rakesh, Palepu Narasimha. "A Data Science Approach to Bioinformatics." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 3860–69. http://dx.doi.org/10.22214/ijraset.2021.37221.

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Computer aided drug design (CADD) which uses the computational advance towards to develop, discover and scrutinize and examine drugs and alike biologically agile molecules. CADD is a specialized stream which uses the computational techniques to mimic drug-receptor interactions. CADD procedures are so much dependent on the tools of bioinformatics, databases & applications. There are so many advantages of computer aided drug discovery; it saves lot of time which is one of the main advantages followed by low cost and more accuracy. CADD required less manpower to work. There are different type

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Ayon, Navid J. "High-Throughput Screening of Natural Product and Synthetic Molecule Libraries for Antibacterial Drug Discovery." Metabolites 13, no. 5 (2023): 625. http://dx.doi.org/10.3390/metabo13050625.

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Due to the continued emergence of resistance and a lack of new and promising antibiotics, bacterial infection has become a major public threat. High-throughput screening (HTS) allows rapid screening of a large collection of molecules for bioactivity testing and holds promise in antibacterial drug discovery. More than 50% of the antibiotics that are currently available on the market are derived from natural products. However, with the easily discoverable antibiotics being found, finding new antibiotics from natural sources has seen limited success. Finding new natural sources for antibacterial

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Conrad, Jay, and Roy J. Vaz. "Validating the Use of Rational Modification of Compounds to Reduce P-gp Efflux." Archives of Pharmacology and Therapeutics 6, no. 1 (2024): 34–39. http://dx.doi.org/10.33696/pharmacol.6.054.

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In both the Central nervous system (CNS) as well as Oncology small molecule drug discovery programs, efflux due to P-glycoprotein (P-gp) could be a deterrent during the discovery phase to obtain in vitro or in vivo pharmacological readouts. Several different strategies have been utilized in the past in order to overcome efflux by P-gp, many of which have been described in a recent article [5] from our labs. We describe the use of Induced-fit docking (IFD) of matched pairs (pairs of molecules modified by a single group) in order to demonstrate that a change in the IFD score, destabilizing the c

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Gupta, Sayan D., Pappu S. Swapanthi, Deshetti Bhagya, et al. "Rational Identification of Hsp90 Inhibitors as Anticancer Lead Molecules by Structure Based Drug Designing Approach." Anti-Cancer Agents in Medicinal Chemistry 20, no. 3 (2020): 369–85. http://dx.doi.org/10.2174/1871520619666191111152050.

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Background: Heat shock protein 90 (Hsp90) is an encouraging anticancer target for the development of clinically significant molecules. Schiff bases play a crucial role in anticancer research because of their ease of synthesis and excellent antiproliferative effect against multiple cancer cell lines. Therefore, we started our research work with the discovery of resorcinol/4-chloro resorcinol derived Schiff bases as Hsp90 inhibitors, which resulted in the discovery of a viable anticancer lead molecule. Objective: The objective of the study is to discover more promising lead molecules using our p

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P.L.Sujatha, K.Anbu Kumar, P.Devendran, S.P.Preetha, and Manikkavasagan Ilangopathy3. "APPLICATION OF COMPUTATIONAL METHODS IN DRUG DISCOVERY." Indian Journal of Veterinary and Animal Sciences Research 53, no. 5 (2025): 1–8. https://doi.org/10.56093/ijvasr.v53i5.161975.

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Rational drug design, is the inventive process of finding new medications based on knowledge of the biological target. Drug design involves the design of small molecules that are complementary in shape and charge to the bimolecular target to which they interact and therefore will bind to it. In the experiment based approach, drugs are discovered through trial and error. With high R&D cost and consumption, computational drug discovery helps scientists gain insight into drug receptor interactions and reduce time and cost. Scientists can predict whether the molecule will succeed or fail in th

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Kumar, Sethu Arun, Thirumoorthy Durai Ananda Kumar, Narasimha M. Beeraka, et al. "Machine learning and deep learning in data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry." Future Medicinal Chemistry 14, no. 4 (2022): 245–70. http://dx.doi.org/10.4155/fmc-2021-0243.

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Predicting novel small molecule bioactivities for the target deconvolution, hit-to-lead optimization in drug discovery research, requires molecular representation. Previous reports have demonstrated that machine learning (ML) and deep learning (DL) have substantial implications in virtual screening, peptide synthesis, drug ADMET screening and biomarker discovery. These strategies can increase the positive outcomes in the drug discovery process without false-positive rates and can be achieved in a cost-effective way with a minimum duration of time by high-quality data acquisition. This review s

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Febrina, Ellin, and Aiyi Asnawi. "Lead compound discovery using pharmacophore-based models of small-molecule metabolites from human blood as inhibitor cellular entry of SARS-CoV-2." Journal of Pharmacy & Pharmacognosy Research 11, no. 5 (2023): 810–22. http://dx.doi.org/10.56499/jppres23.1688_11.5.810.

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Context: The development of emerging viral diseases like SARS-CoV-2 has underlined the critical need for new antiviral medicines. Many of the discovered inhibitors have off-target effects or toxicity issues, but no single lead chemical has been found as a powerful SARS-CoV-2 inhibitor. Small-molecule metabolites from human blood, for example, have been demonstrated to exhibit biological action, such as anti-inflammatory or antiviral properties, but have not been reported as pharmacophore-based drug discovery models. Aims: To evaluate the feasibility of employing pharmacophore models of small-m

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Venkatraman, Vishwesh, Jeremiah Gaiser, Daphne Demekas, Amitava Roy, Rui Xiong, and Travis J. Wheeler. "Do Molecular Fingerprints Identify Diverse Active Drugs in Large-Scale Virtual Screening? (No)." Pharmaceuticals 17, no. 8 (2024): 992. http://dx.doi.org/10.3390/ph17080992.

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Computational approaches for small-molecule drug discovery now regularly scale to the consideration of libraries containing billions of candidate small molecules. One promising approach to increased the speed of evaluating billion-molecule libraries is to develop succinct representations of each molecule that enable the rapid identification of molecules with similar properties. Molecular fingerprints are thought to provide a mechanism for producing such representations. Here, we explore the utility of commonly used fingerprints in the context of predicting similar molecular activity. We show t

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Wang, Huibin, Zehui Wang, Minghua Shi, Zixian Cheng, and Ying Qian. "Enhancing Unconditional Molecule Generation via Online Knowledge Distillation of Scaffolds." Molecules 30, no. 6 (2025): 1262. https://doi.org/10.3390/molecules30061262.

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Generating new drug-like molecules is an essential aspect of drug discovery, and deep learning models significantly accelerate this process. Language models have demonstrated great potential in generating novel and realistic SMILES representations of molecules. Molecular scaffolds, which serve as the key structural foundation, can facilitate language models in discovering chemically feasible and biologically relevant molecules. However, directly using scaffolds as prior inputs can introduce bias, thereby limiting the exploration of novel molecules. To combine the above advantages and address t

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