COMPUTATIONAL APPROACHES FOR DRUG DISCOVERY FROM MEDICINAL PLANTS IN THE ERA OF DATA DRIVEN RESEARCH

INDIAN DRUGS ◽  
2021 ◽  
Vol 58 (08) ◽  
pp. 7-23
Author(s):  
Pratibha Pansari ◽  
Keyword(s):  
Drug Discovery ◽  
Medicinal Plants ◽  
In Silico ◽  
Drug Target ◽  
Target Gene ◽  
Scientific Work ◽  
Data Driven ◽  
Network Pharmacology ◽  
Gene Pathway ◽  
Topological Network

The significant scientific work on the development of bio-active compound databases, computational technologies, and the integration of Information Technology with Biotechnology has brought a revolution in the domain of drug discovery. These tools facilitate the medicinal plant-based in silico drug discovery, which has become the frontier of pharmacological science. In this review article, we elucidate the methodology of in silico drug discovery for the medicinal plants and present an outlook on recent tools and technologies. Further, we explore the multi-component, multi-target, and multi-pathway mechanism of the bio-active compounds with the help of Network Pharmacology, which enables us to create a topological network between drug, target, gene, pathway, and disease.

scholarly journals Open-source chemogenomic data-driven algorithms for predicting drug–target interactions

2018 ◽  
Vol 20 (4) ◽  
pp. 1465-1474 ◽  
Author(s):  
Ming Hao ◽  
Stephen H Bryant ◽  
Yanli Wang
Keyword(s):  
Drug Discovery ◽  
Human Genome ◽  
High Throughput Screening ◽  
Drug Target ◽  
Drug Targets ◽  
Drug Repositioning ◽  
Research Field ◽  
Data Driven ◽  
Source Codes ◽  
Novel Technologies

AbstractWhile novel technologies such as high-throughput screening have advanced together with significant investment by pharmaceutical companies during the past decades, the success rate for drug development has not yet been improved prompting researchers looking for new strategies of drug discovery. Drug repositioning is a potential approach to solve this dilemma. However, experimental identification and validation of potential drug targets encoded by the human genome is both costly and time-consuming. Therefore, effective computational approaches have been proposed to facilitate drug repositioning, which have proved to be successful in drug discovery. Doubtlessly, the availability of open-accessible data from basic chemical biology research and the success of human genome sequencing are crucial to develop effective in silico drug repositioning methods allowing the identification of potential targets for existing drugs. In this work, we review several chemogenomic data-driven computational algorithms with source codes publicly accessible for predicting drug–target interactions (DTIs). We organize these algorithms by model properties and model evolutionary relationships. We re-implemented five representative algorithms in R programming language, and compared these algorithms by means of mean percentile ranking, a new recall-based evaluation metric in the DTI prediction research field. We anticipate that this review will be objective and helpful to researchers who would like to further improve existing algorithms or need to choose appropriate algorithms to infer potential DTIs in the projects. The source codes for DTI predictions are available at: https://github.com/minghao2016/chemogenomicAlg4DTIpred.

Chemo- and bioinformatics resources for in silico drug discovery from medicinal plants beyond their traditional use: a critical review

2014 ◽  
Vol 31 (11) ◽  
pp. 1585-1611 ◽  
Author(s):  
Alexey A. Lagunin ◽  
Rajesh K. Goel ◽  
Dinesh Y. Gawande ◽  
Priynka Pahwa ◽  
Tatyana A. Gloriozova ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Medicinal Plants ◽  
Critical Review ◽  
In Silico ◽  
Therapeutic Potential ◽  
Traditional Use

An overview of databases andin silicotools for discovery of the hidden therapeutic potential of medicinal plants.

Chemogenomic Approaches for Revealing Drug target Interactions in Drug Discovery

2021 ◽  
Vol 22 ◽  
Author(s):  
Harshita Bhargava ◽  
Amita Sharma ◽  
Prashanth Suravajhala
Keyword(s):  
Drug Discovery ◽  
In Silico ◽  
Drug Target ◽  
Drug Repositioning ◽  
Personalised Medicine ◽  
Classification Problem ◽  
Target Interaction ◽  
Advantages And Disadvantages

: The drug discovery process has been a crucial and cost-intensive process. This cost is not only monetary but also involves risks, time, and labour that are incurred while introducing a drug in the market. In order to reduce this cost and the risks associated with the drugs that may result in severe side effects, the in silico methods have gained popularity in recent years. These methods have had a significant impact on not only drug discovery but also the related areas such as drug repositioning, drug-target interaction prediction, drug side effect prediction, personalised medicine, etc. Amongst these research areas predicting interactions between drugs and targets forms the basis for drug discovery. The availability of big data in the form of bioinformatics, genetic databases, along with computational methods, have further supported data-driven decision-making. The results obtained through these methods may be further validated using in vitro or in vivo experiments. This validation step can further justify the predictions resulting from in silico approaches, further increasing the accuracy of the overall result in subsequent stages. A variety of approaches are used in predicting drug-target interactions, including ligand-based, molecular docking based and chemogenomic-based approaches. This paper discusses the chemogenomic methods, considering drug target interaction as a classification problem on whether or not an interaction between a particular drug and target would serve as a basis for understanding drug discovery/drug repositioning. We present the advantages and disadvantages associated with their application.

scholarly journals Target2drug : A Novel Programmatic Workflow to Automate in Silico Drug Discovery

2020 ◽  
Author(s):  
Ben Geoffrey A S ◽  
Rafal Madaj ◽  
Akhil Sanker ◽  
Pavan Preetham Valluri ◽  
Judith Gracia ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Computational Biology ◽  
In Silico ◽  
Drug Target ◽  
Drug Targets ◽  
Qsar Model ◽  
Learning Tools ◽  
Active Compounds ◽  
Qsar Modelling ◽  
Drug Leads

As the Big Data and Artificial Intelligence (AI) revolution continues to affect every area of our lives, it’s influence is also exerted in the areas of bioinformatics, computational biology and drug discovery. Machine/Deep Learning tools have been developed to predict compounds-drug target interactions and the vice-versa process of predicting target interactions for an compound. In our presented work, we report a programmatic tool, which incorporates many features of the bioinformatics, computational biology and AI-driven drug discovery revolutions into a single workflow assembly. When a user is required to identify drugs against a new drug target, the user provides target signatures in the form of amino acid sequence of the target or it’s corresponding nucleotide sequence as input to the tool and the tool carries out a BLAST protocol to identify known protein drug targets that are similar to the new target submitted by the user and collects data linked to the target involving, active compounds against the target, the activity value and molecular descriptors of active compounds to perform QSAR modelling and to generate drug leads with predictions from the validated QSAR model. The tool performs an In-Silico modelling to generate In-Silico interaction profiles of compounds generated as drug leads and the target and stores the results in the working folder of the user. To demonstrate the use of the tool, we have carried out a demonstration with the target signatures of the current pandemic causing virus, SARS-CoV 2. However the tool can be used against any target and is expected to help in growing our knowledge graph of targets and interacting compounds. <br>

scholarly journals QPoweredTarget2DeNovoDrugPropMax : a novel programmatic tool incorporating deep learning and in silico methods for automated de novo drug design for any target of interest

2021 ◽  
Author(s):  
Ben Geoffrey ◽  
Rafal Madaj ◽  
Pavan Preetham Valluri ◽  
Akhil Sanker
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Drug Discovery ◽  
In Silico ◽  
Drug Target ◽  
Data Science ◽  
De Novo ◽  
Autodock Vina ◽  
De Novo Drug Design ◽  
Ligand Interaction

The past decade has seen a surge in the range of application data science, machine learning, deep learning, and AI methods to drug discovery. The presented work involves an assemblage of a variety of AI methods for drug discovery along with the incorporation of in silico techniques to provide a holistic tool for automated drug discovery. When drug candidates are required to be identified for aparticular drug target of interest, the user is required to provide the tool target signatures in the form of an amino acid sequence or its corresponding nucleotide sequence. The tool collects data registered on PubChem required to perform an automated QSAR and with the validated QSAR model, prediction and drug lead generation are carried out. This protocol we call Target2Drug. This is followed by a protocol we call Target2DeNovoDrug wherein novel molecules with likely activityagainst the target are generated de novo using a generative LSTM model. It is often required in drug discovery that the generated molecules possess certain properties like drug-likeness, and therefore to optimize the generated de novo molecules toward the required drug-like property we use a deep learning model called DeepFMPO, and this protocol we call Target2DeNovoDrugPropMax. This is followed by the fast automated AutoDock-Vina based in silico modeling and profiling of theinteraction of optimized drug leads and the drug target. This is followed by an automated execution of the Molecular Dynamics protocol that is also carried out for the complex identified with the best protein-ligand interaction from the AutoDock- Vina based virtual screening. The results are stored in the working folder of the user. The code is maintained, supported, and provide for use in thefollowing GitHub repositoryhttps://github.com/bengeof/Target2DeNovoDrugPropMaxAnticipating the rise in the use of quantum computing and quantum machine learning in drug discovery we use the Penny-lane interface to quantum hardware to turn classical Keras layers used in our machine/deep learning models into a quantum layer and introduce quantum layers into our classical models to produce a quantum-classical machine/deep learning hybrid model of our tool and the code corresponding to the same is provided belowhttps://github.com/bengeof/QPoweredTarget2DeNovoDrugPropMax

scholarly journals Target2drug : A Novel Programmatic Workflow to Automate in Silico Drug Discovery

2020 ◽  
Author(s):  
Ben Geoffrey A S ◽  
Rafal Madaj ◽  
Akhil Sanker ◽  
Pavan Preetham Valluri ◽  
Judith Gracia ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Computational Biology ◽  
In Silico ◽  
Drug Target ◽  
Drug Targets ◽  
Qsar Model ◽  
Learning Tools ◽  
Active Compounds ◽  
Qsar Modelling ◽  
Drug Leads

As the Big Data and Artificial Intelligence (AI) revolution continues to affect every area of our lives, it’s influence is also exerted in the areas of bioinformatics, computational biology and drug discovery. Machine/Deep Learning tools have been developed to predict compounds-drug target interactions and the vice-versa process of predicting target interactions for an compound. In our presented work, we report a programmatic tool, which incorporates many features of the bioinformatics, computational biology and AI-driven drug discovery revolutions into a single workflow assembly. When a user is required to identify drugs against a new drug target, the user provides target signatures in the form of amino acid sequence of the target or it’s corresponding nucleotide sequence as input to the tool and the tool carries out a BLAST protocol to identify known protein drug targets that are similar to the new target submitted by the user and collects data linked to the target involving, active compounds against the target, the activity value and molecular descriptors of active compounds to perform QSAR modelling and to generate drug leads with predictions from the validated QSAR model. The tool performs an In-Silico modelling to generate In-Silico interaction profiles of compounds generated as drug leads and the target and stores the results in the working folder of the user. To demonstrate the use of the tool, we have carried out a demonstration with the target signatures of the current pandemic causing virus, SARS-CoV 2. However the tool can be used against any target and is expected to help in growing our knowledge graph of targets and interacting compounds. <br>

Molecular mechanics approaches for rational drug design: forcefields and solvation models

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Boris D. Bekono ◽  
Alfred N. Sona ◽  
Donatus B. Eni ◽  
Luc C. O. Owono ◽  
Eugène Megnassan ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Molecular Mechanics ◽  
In Silico ◽  
Drug Target ◽  
Energy Equation ◽  
Rational Drug Design ◽  
Rational Drug ◽  
Structures And Properties ◽  
Solvation Models

Abstract The use of molecular mechanics (MM) in understanding the energy and target of a drug, its structures, and properties has increased recently. This is achieved by the formulation of a simple MM energy equation, which represents the sum of the different energy interactions, often referred to as “forcefields” (FFs). The concept of FFs is now widely used as one of the fundamental tools for the in silico prediction of drug-target interactions. To generate more accurate predictions in the in silico drug discovery projects, the solvent effects are often taken into account. This review seeks to present an introductory guide for the reader on the fundamentals of MM with special emphasis on the role of FFs and the solvation models.

Identification of active phytochemical from traditional herbal knowledgebase targeting Pantothenate synthetase for anti-tuberculosis therapy

2021 ◽  
Vol 18 ◽  
Author(s):  
Saurov Mahanta ◽  
Bhaskarjyoti Gogoi ◽  
Pankaj Chetia ◽  
Bhaben Tanti ◽  
Pratap Jyoti Handique
Keyword(s):  
Drug Discovery ◽  
Camellia Sinensis ◽  
In Silico ◽  
Drug Target ◽  
Dynamics Simulation ◽  
Drug Resistant ◽  
Data Set ◽  
Pantothenate Synthetase ◽  
Ic50 Value ◽  
The Right

: Increased numbers of reported cases of Mycobacterium tuberculosis (Mtb) resistance to the generally used antibiotics demand to identify novel therapeutic entities for better control of Tuberculosis. Most of the Structure-based Drug Discovery (SBDD) works reported earlier had screened compounds against a single drug target to avoid any off-target binding and related complications. Because of the development of Multi-Drug Resistant and Extensively Drug-Resistant strains of Mtb and looking into the incurable pathologies, targeting the right drug target with a promising ligand data set will result in effective therapeutics. Simultaneously, traditional knowledge-based drugs have earned little success in developing anti-tuberculosis drugs in recent studies. Combining the right-target approach and traditional herbal knowledge base, this in-silico drug discovery study was carried out where 1236 compounds from two plants, traditionally used for TB treatment, Camellia sinensis, Ginkgo biloba along with the antibacterial compounds of DrugBank Database have been screened against Pantothenate synthetase of Mtb, a well-known drug target for anti-TB therapeutics. Through this analytics, Epigallocatechin gallate (EGCG) of Camellia sinensis has been reported through in silico docking studies and subsequent Molecular Dynamics simulation, as a promising anti-TB candidate due to its affinity towards Pantothenate synthetase of Mtb. EGCG was subjected to ADME-Tox studies as well as 3D QSAR analysis for the detection of its drug-like properties for the determination of IC50 value. The EGCG showed the IC50 value at 1404 nM, which is quite promising for a plant-origin compound. The selected ligand, EGCG, due to its promising affinity towards Pantothenate synthetase of Mtb with high drug-like properties, justifies its selection as a potential anti-tuberculosis compound.

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