Andrographis paniculata (Burm. f.) Wall. ex Nees: An Updated Review of Phytochemistry, Antimicrobial Pharmacology, and Clinical Safety and Efficacy

S Hossain, Z Urbi, H Karuniawati, RB Mohiuddin… - Life, 2021 - mdpi.com
Infectious disease (ID) is one of the top-most serious threats to human health globally,
further aggravated by antimicrobial resistance and lack of novel immunization options …

Machine learning approaches and databases for prediction of drug–target interaction: a survey paper

M Bagherian, E Sabeti, K Wang… - Briefings in …, 2021 - academic.oup.com
The task of predicting the interactions between drugs and targets plays a key role in the
process of drug discovery. There is a need to develop novel and efficient prediction …

Network medicine framework for identifying drug-repurposing opportunities for COVID-19

D Morselli Gysi, Í Do Valle, M Zitnik, A Ameli… - Proceedings of the …, 2021 - pnas.org
The COVID-19 pandemic has highlighted the need to quickly and reliably prioritize clinically
approved compounds for their potential effectiveness for severe acute respiratory syndrome …

Network-based prediction of drug combinations

F Cheng, IA Kovács, AL Barabási - Nature communications, 2019 - nature.com
Drug combinations, offering increased therapeutic efficacy and reduced toxicity, play an
important role in treating multiple complex diseases. Yet, our ability to identify and validate …

[HTML][HTML] Artificial intelligence in pharmaceutical sciences

M Lu, J Yin, Q Zhu, G Lin, M Mou, F Liu, Z Pan, N You… - Engineering, 2023 - Elsevier
Drug discovery and development affects various aspects of human health and dramatically
impacts the pharmaceutical market. However, investments in a new drug often go …

Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases

AS Rifaioglu, H Atas, MJ Martin… - Briefings in …, 2019 - academic.oup.com
The identification of interactions between drugs/compounds and their targets is crucial for
the development of new drugs. In vitro screening experiments (ie bioassays) are frequently …

Deep learning improves prediction of drug–drug and drug–food interactions

JY Ryu, HU Kim, SY Lee - Proceedings of the national academy of …, 2018 - pnas.org
Drug interactions, including drug–drug interactions (DDIs) and drug–food constituent
interactions (DFIs), can trigger unexpected pharmacological effects, including adverse drug …

Discovering Anti-Cancer Drugs via Computational Methods

W Cui, A Aouidate, S Wang, Q Yu, Y Li… - Frontiers in …, 2020 - frontiersin.org
New drug discovery has been acknowledged as a complicated, expensive, time-consuming,
and challenging project. It has been estimated that around 12 years and 2.7 billion USD, on …

Network-based approach to prediction and population-based validation of in silico drug repurposing

F Cheng, RJ Desai, DE Handy, R Wang… - Nature …, 2018 - nature.com
Here we identify hundreds of new drug-disease associations for over 900 FDA-approved
drugs by quantifying the network proximity of disease genes and drug targets in the human …

[HTML][HTML] Computational methods in drug discovery

SP Leelananda, S Lindert - Beilstein journal of organic …, 2016 - beilstein-journals.org
The process for drug discovery and development is challenging, time consuming and
expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut …