| nr |
titel |
auteur |
tijdschrift |
jaar |
jaarg. |
afl. |
pagina('s) |
type |
| 1 |
AI in drug development: a multidisciplinary perspective
|
Gallego, Víctor
|
|
|
25 |
3 |
p. 1461-1479
|
artikel
|
| 2 |
A multi-conformational virtual screening approach based on machine learning targeting PI3Kγ
|
Zhu, Jingyu
|
|
|
25 |
3 |
p. 1271-1282
|
artikel
|
| 3 |
A multimodal deep learning-based drug repurposing approach for treatment of COVID-19
|
Hooshmand, Seyed Aghil
|
|
|
25 |
3 |
p. 1717-1730
|
artikel
|
| 4 |
A novel artificial intelligence protocol to investigate potential leads for diabetes mellitus
|
Gong, Jia-Ning
|
|
|
25 |
3 |
p. 1375-1393
|
artikel
|
| 5 |
Applications of artificial intelligence to drug design and discovery in the big data era: a comprehensive review
|
Tripathi, Neetu
|
|
|
25 |
3 |
p. 1643-1664
|
artikel
|
| 6 |
Artificial intelligence to deep learning: machine intelligence approach for drug discovery
|
Gupta, Rohan
|
|
|
25 |
3 |
p. 1315-1360
|
artikel
|
| 7 |
A simple and robust model to predict the inhibitory activity of α-glucosidase inhibitors through combined QSAR modeling and molecular docking techniques
|
Izadpanah, Elaheh
|
|
|
25 |
3 |
p. 1811-1825
|
artikel
|
| 8 |
Classification models and SAR analysis on CysLT1 receptor antagonists using machine learning algorithms
|
Wang, Hongzhao
|
|
|
25 |
3 |
p. 1597-1616
|
artikel
|
| 9 |
Computational assessment of saikosaponins as adjuvant treatment for COVID-19: molecular docking, dynamics, and network pharmacology analysis
|
Chikhale, Rupesh
|
|
|
25 |
3 |
p. 1889-1904
|
artikel
|
| 10 |
Computational guided identification of a citrus flavonoid as potential inhibitor of SARS-CoV-2 main protease
|
Gogoi, Neelutpal
|
|
|
25 |
3 |
p. 1745-1759
|
artikel
|
| 11 |
Computational strategies for the discovery of biological functions of health foods, nutraceuticals and cosmeceuticals: a review
|
Carpio, Laureano E.
|
|
|
25 |
3 |
p. 1425-1438
|
artikel
|
| 12 |
Convolutional neural networks (CNNs): concepts and applications in pharmacogenomics
|
Vaz, Joel Markus
|
|
|
25 |
3 |
p. 1569-1584
|
artikel
|
| 13 |
Discovery of novel DGAT1 inhibitors by combination of machine learning methods, pharmacophore model and 3D-QSAR model
|
Zhang, Hui
|
|
|
25 |
3 |
p. 1481-1495
|
artikel
|
| 14 |
Ensemble learning application to discover new trypanothione synthetase inhibitors
|
Alice, Juan I.
|
|
|
25 |
3 |
p. 1361-1373
|
artikel
|
| 15 |
Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery
|
Tripathi, Manish Kumar
|
|
|
25 |
3 |
p. 1439-1460
|
artikel
|
| 16 |
Exploring the dynamic mechanism of allosteric drug SHP099 inhibiting SHP2E69K
|
Du, Shan
|
|
|
25 |
3 |
p. 1873-1887
|
artikel
|
| 17 |
First structure–activity relationship analysis of SARS-CoV-2 virus main protease (Mpro) inhibitors: an endeavor on COVID-19 drug discovery
|
Amin, Sk. Abdul
|
|
|
25 |
3 |
p. 1827-1838
|
artikel
|
| 18 |
Hemagglutinin-esterase cannot be considered as a candidate for designing drug against COVID-19
|
Zandi, Milad
|
|
|
25 |
3 |
p. 1999-2000
|
artikel
|
| 19 |
Identification of kinase inhibitors that rule out the CYP27B1-mediated activation of vitamin D: an integrated machine learning and structure-based drug designing approach
|
Mahajan, Kanupriya
|
|
|
25 |
3 |
p. 1617-1641
|
artikel
|
| 20 |
Identification of novel inhibitors of angiotensin-converting enzyme 2 (ACE-2) receptor from Urtica dioica to combat coronavirus disease 2019 (COVID-19)
|
Upreti, Shobha
|
|
|
25 |
3 |
p. 1795-1809
|
artikel
|
| 21 |
Identification of potential plant-based inhibitor against viral proteases of SARS-CoV-2 through molecular docking, MM-PBSA binding energy calculations and molecular dynamics simulation
|
Gogoi, Bhaskarjyoti
|
|
|
25 |
3 |
p. 1963-1977
|
artikel
|
| 22 |
In silico design of novel aptamers utilizing a hybrid method of machine learning and genetic algorithm
|
Torkamanian-Afshar, Mahsa
|
|
|
25 |
3 |
p. 1395-1407
|
artikel
|
| 23 |
In silico prediction of chemical-induced hematotoxicity with machine learning and deep learning methods
|
Hua, Yuqing
|
|
|
25 |
3 |
p. 1585-1596
|
artikel
|
| 24 |
Logistic matrix factorisation and generative adversarial neural network-based method for predicting drug-target interactions
|
Abbou, Sarra Itidal
|
|
|
25 |
3 |
p. 1497-1516
|
artikel
|
| 25 |
Machine learning models for classification tasks related to drug safety
|
Rácz, Anita
|
|
|
25 |
3 |
p. 1409-1424
|
artikel
|
| 26 |
Machine learning models to select potential inhibitors of acetylcholinesterase activity from SistematX: a natural products database
|
Herrera-Acevedo, Chonny
|
|
|
25 |
3 |
p. 1553-1568
|
artikel
|
| 27 |
Modeling and simulation study to identify threonine synthase as possible drug target in Leishmania major
|
Meshram, Rohan J.
|
|
|
25 |
3 |
p. 1679-1700
|
artikel
|
| 28 |
Molecular docking, binding mode analysis, molecular dynamics, and prediction of ADMET/toxicity properties of selective potential antiviral agents against SARS-CoV-2 main protease: an effort toward drug repurposing to combat COVID-19
|
Rai, Himanshu
|
|
|
25 |
3 |
p. 1905-1927
|
artikel
|
| 29 |
Molecular docking studies, molecular dynamics and ADME/tox reveal therapeutic potentials of STOCK1N-69160 against papain-like protease of SARS-CoV-2
|
Elekofehinti, Olusola Olalekan
|
|
|
25 |
3 |
p. 1761-1773
|
artikel
|
| 30 |
Molecular insights on ABL kinase activation using tree-based machine learning models and molecular docking
|
Fernandes, Philipe Oliveira
|
|
|
25 |
3 |
p. 1301-1314
|
artikel
|
| 31 |
Predicting blood-to-plasma concentration ratios of drugs from chemical structures and volumes of distribution in humans
|
Mamada, Hideaki
|
|
|
25 |
3 |
p. 1261-1270
|
artikel
|
| 32 |
Predicting HIV drug resistance using weighted machine learning method at target protein sequence-level
|
Cai, Qihang
|
|
|
25 |
3 |
p. 1541-1551
|
artikel
|
| 33 |
QSAR modeling without descriptors using graph convolutional neural networks: the case of mutagenicity prediction
|
Hung, Chiakang
|
|
|
25 |
3 |
p. 1283-1299
|
artikel
|
| 34 |
Recent trends in artificial intelligence-driven identification and development of anti-neurodegenerative therapeutic agents
|
Kashyap, Kushagra
|
|
|
25 |
3 |
p. 1517-1539
|
artikel
|
| 35 |
RETRACTED ARTICLE: CoViTris2020 and ChloViD2020: a striking new hope in COVID-19 therapy
|
Rabie, Amgad M.
|
|
|
25 |
3 |
p. 1839-1854
|
artikel
|
| 36 |
Special issue of molecular diversity on “AI and ML for small molecule drug discovery in the big data era”
|
Roy, Kunal
|
|
|
25 |
3 |
p. 1259-1260
|
artikel
|
| 37 |
Structural based screening of potential inhibitors of SMAD4: a step towards personalized medicine for gall bladder and other associated cancers
|
Kumar, Rakesh
|
|
|
25 |
3 |
p. 1945-1961
|
artikel
|
| 38 |
Structural modification of 4, 5-dihydro-[1, 2, 4] triazolo [4, 3-f] pteridine derivatives as BRD4 inhibitors using 2D/3D-QSAR and molecular docking analysis
|
Tong, Jian-Bo
|
|
|
25 |
3 |
p. 1855-1872
|
artikel
|
| 39 |
Structure-based identification of SARS-CoV-2 main protease inhibitors from anti-viral specific chemical libraries: an exhaustive computational screening approach
|
Bhowmick, Shovonlal
|
|
|
25 |
3 |
p. 1979-1997
|
artikel
|
| 40 |
Structure-based screening of novel lichen compounds against SARS Coronavirus main protease (Mpro) as potentials inhibitors of COVID-19
|
Joshi, Tanuja
|
|
|
25 |
3 |
p. 1665-1677
|
artikel
|
| 41 |
Therapeutic p28 peptide targets essential H1N1 influenza virus proteins: insights from docking and molecular dynamics simulations
|
Sasidharan, Santanu
|
|
|
25 |
3 |
p. 1929-1943
|
artikel
|
| 42 |
Understanding the enzymatic inhibition of intestinal alkaline phosphatase by aminophenazone-derived aryl thioureas with aided computational molecular dynamics simulations: synthesis, characterization, SAR and kinetic profiling
|
Khurshid, Asma
|
|
|
25 |
3 |
p. 1701-1715
|
artikel
|
| 43 |
Using Chou’s 5-steps rule to study pharmacophore-based virtual screening of SARS-CoV-2 Mpro inhibitors
|
Pundir, Hemlata
|
|
|
25 |
3 |
p. 1731-1744
|
artikel
|
| 44 |
Virtual screening and drug repurposing experiments to identify potential novel selective MAO-B inhibitors for Parkinson’s disease treatment
|
Crisan, Luminita
|
|
|
25 |
3 |
p. 1775-1794
|
artikel
|
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