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The drug industry is one of the major players guiding the development of the medicines, biotechnology & pharmacology field. Drug discovery is the process by which drugs are discovered and designed. It is a process which aims at identifying a compound therapeutically useful in curing & treating disease. The process of drug discovery involves the ide...
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Data indicate that the widely touted “innovation crisis” in pharmaceuticals is a myth. The real innovation crisis, say Donald Light and Joel Lexchin , stems from current incentives that reward companies for developing large numbers of new drugs with few clinical advantages over existing ones
Since the early 2000s, industry leaders, observers, and...
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... Validation of drug targets is the process of physiological, pathological, and pharmacological evaluation of a biomolecule, both at the molecular and phenotypic levels [24]. The process generally comprises understanding the relationship between target and the disease as well as designing a bioassay to measure biological activity [25]. The failure or emergence of a drug candidate during or after clinical trials is dependent on efficacy that is largely hinged on the relationship of target to disease process and molecular interactions between targets and leads. ...
A number of bioactive natural products with therapeutic potential are naturally available. Drug discovery and development from these products using conventional methods is time and resource consuming. In order to improve efficiency of identifying new drugs from natural products, Computer Aided Drug Design (CADD) or in silico screening is now routinely combined with chemical and biological research. In silico screening represents an interdisciplinary effort to speed up the drug discovery process through the identification and evaluation of safe active drug candidates. With the development of computational technologies, CADD approaches are progressively becoming more common. In this chapter, we discuss steps in CADD and new approaches used in the discovery of bioactive natural products like ligand based drug design, structure based drug design, quantitative structure activity relationship (QSAR) and quantitative structure-property relationships (QSPR).
... The drug discovery process involves the identification of candidates, synthesis, characterization, screening and assays for therapeutic efficacy. The new in silico approach is being tried to understand how disease and infection are controlled the molecular and target specific entities based on this knowledge 22 . Therefore, in the present work, we applied computational methods for lead generation and lead optimization in the drug discovery process. ...
Drug discovery (DD) has an unknown history since the origin of mankind with the process of the trial-and-error method. Modern-day DD primarily straddles three main periods, i.e., the nineteenth century, based on DD by chance by the medicinal chemists; the twentieth century, which spans the exploration and reporting of new drug structures; and finally, the twenty-first century, in which all known structures in conjunction with novel techniques, viz., molecular modeling, combinatorial chemistry, and automated high-throughput screening, led to huge advances in DD. In the start, the scientists examine the natural products themselves to find the exact effects. The isolation of active phytochemicals started in the early nineteenth century, while the advancement in chemical and biological sciences has led the modern DD and development. Moreover, recombinant DNA technology revolutionized the development of potential drugs with higher accuracy and precision. In addition, the onset of the “omics” (proteomics, genomics, metabolomics, etc.) era has boosted the increase in biopharmaceutical drugs approved by the FDA/EMEA for clinical uses. Currently, digital and disruptive technologies such as network pharmacology and molecular docking studies are changing the scenario of DD and development by producing more efficient, personalized drugs with little or no harm at all.
New chemical entities of quinazolinone derivatives were used to design as antibacterial agents through selective inhibitors of X‐ray crystal structure of Staphylococcus aureus (1T2W) sortase A by the molecular docking using V‐Life Sciences MDS version 4.6, software. It is successfully reproduced the binding approach of the crystal structure of the Staphylococcus aureus antagonists. The docking results suggested that the modification in the series that gives better binding potential, hydrophobic Van der Waals, H‐bond and charge interactions are responsible for forming the stable compounds of the ligands with receptor. It has been observed that the ligands numbers 4k , 4l , and 4m possess a minimum docking score i.e. minimum binding energy in kilocalorie per mole i.e. these molecules have more affinity for active site of receptor. 2D and 3D QSAR analyses were carried out on quinazoline‐4‐one derivatives for their antimicrobial activities on S. aureus . The activity of the molecules was transformed into log 1/C. The statistically significant of 2DQSAR and 3D QSAR models are r ² = 0.8066 and q ² = 0.6789 and internal (q ² = 0.7157) and external (predictive r ² = 0.4634), respectively. This study revealed that the major contributing descriptors of 2D QSAR studies are DeltaEpsilonB and DeltaPsiA and 3D QSAR model proves the steric as well as electrostatic effects determine the binding affinity for the drug development. The results of the current computational studies are useful for further designing novel chemical entities of anti‐microbial agent.
Traditional drug development is often high-risk and time-consuming. A promising alternative is to reuse or relocate approved drugs. Recently, some methods based on graph representation learning have started to be used for drug repositioning. These models learn the low dimensional embeddings of drug and disease nodes from the drug-disease interaction network to predict the potential association between drugs and diseases. However, these methods have strict requirements for the dataset, and if the dataset is sparse, the performance of these methods will be severely affected. At the same time, these methods have poor robustness to noise in the dataset. In response to the above challenges, we propose a drug repositioning model based on self-supervised graph learning with adptive denoising, called SADR. SADR uses data augmentation and contrastive learning strategies to learn feature representations of nodes, which can effectively solve the problems caused by sparse datasets. SADR includes an adaptive denoising training (ADT) component that can effectively identify noisy data during the training process and remove the impact of noise on the model. We have conducted comprehensive experiments on three datasets and have achieved better prediction accuracy compared to multiple baseline models. At the same time, we propose the top 10 new predictive approved drugs for treating two diseases. This demonstrates the ability of our model to identify potential drug candidates for disease indications. The code implementation is available at
https://github.com/Soar1998/SADR
.
Mining of chemical information from large databases of chemical compounds leads to drug discovery. Both supervised and unsupervised algorithms when applied to the databases increase the efficacy of identifying a new target and optimize the lead compound. Machine learning (ML) can be applied to identify the drug target and in optimization of lead compound. ML techniques are proved to reduce the failure rate when compared to traditional methods. This paper mainly works on compound similarity prediction using clustering. Some of the clustering algorithms are k-means, agglomerative hierarchical, fuzzy c-means. Through this compound similarity prediction, the compounds are clustered according to their similarity. So, if the reaction of a new compound is unknown, we can know its reaction through this compound similarity prediction. The primary benefit of compound similarity prediction is that it allows one to predict the reaction of a new compound. We can speed up the drug discovery process if we know how a novel molecule reacts. Agglomerative hierarchical clustering gives best result because the value of Davies–Bouldin index is minimum when compared to k-means, fuzzy c-means clustering algorithm.KeywordsMachine learningDrug discoveryClusteringClustering validation
The goal in this study is to highlight the value of using emergent technologies to support human effort in identifying creative design problems. First, we explore the relationship between design and creativity - a popular concept and an important requirement in engineering design process. A search is conducted across repositories. This includes search in Google, Google Scholar and Google Books databases in addition to others. Findings show that the extent to which the design process requires creativity is somewhat obscure and not generally perceptible. We observe that creativity consists of two aspects: problem-solving and problem-exploring. We also observe that creativity drives the design process, not by the way of problem-solving but by the way of problem-exploring. However, currently, focus is on problem-solving than the equally important problem-exploring. For every 135 studies on problem-solving, there is only one on problem-exploring. Study on problem-exploring is limited. We research further and identify some determinants of the neglect in problem-exploring in design. These determinants are lack of motivation, significant level of difficulty and the presence of many problems yet unsolved. Using the X-Design Process model and Problem-dependent Solution model we show the importance and benefits of problem-exploring in design and why it deserves attention. Consequently, we illustrate the use of emergent technologies to support problem-exploring in design and give reasons why this is possible in Industry 4.0. These technologies include data mining, natural language processing, machine learning, duplication recognition, and so on. We indicate that these technologies will only play subordinate role to humans towards inspiring problem-exploring in design. Also, we state that a precondition to applying these technologies is a study of the human problem-exploring cognition process for subsequent simulation. Success in computational problem-exploring would lead to breakthroughs in global problem-exploring and trigger more creative solutions in coming years.
Plants have traditionally been used as a source lead compounds in the development of drugs. Plants have also been the source of several drugs, which are currently the main stay for the treatment of major diseases like malaria and cancer. This has led to continued research for new drugs, leading to new bioprospecting initiatives for novel lead compounds. However there are challenges that have been encountered in drug discovery from plants, which have led to many pharmaceutical companies shunning away from this venture. In this chapter, we discuss the factors affecting the choice for plant-based products in drug discovery. The main factors considered include; abundance of plant or sources material, efficacy of plant material/ compound, ease of structural modification, molecular size, toxicity, and stability of the compounds. Highlights on how and why each of these factors are considered important have been addressed.
Computational drug repositioning is an important and efficient approach to infer potential indications for drugs, and can improve the efficiency of drug development. The drug repositioning problem essentially is a top-K recommendation task that recommends most likely diseases to drugs based on drug and disease related information. Therefore, many recommendation methods can be adopted to drug repositioning. Collaborative metric learning (CML) algorithm can produce distance metrics that capture the important relationships among objects, and has been applied successfully in recommendation domains. By applying CML in drug repositioning, a joint metric space is learned to encode not only drug preferences for different diseases but also the drug-drug and disease-disease similarity, and can uncover the underlying fine-grained preferences of drugs. In this study, we propose a novel drug repositioning computational method based on Collaborative Metric Learning (CML) to predict novel drug-disease associations based on known drug and disease related information. Specifically, the proposed method learns the latent features of drugs and diseases by applying metric learning, and then predicts the association probability of one drug-disease pair based on the learned features. The comprehensive experimental results show that CMLDR outperforms the other state-of-the-art drug repositioning algorithms in terms of precision, recall, and AUPR.
