Machine learning and deep learning in data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry
SA Kumar, TD Ananda Kumar… - Future Medicinal …, 2022 - Taylor & Francis
Predicting novel small molecule bioactivities for the target deconvolution, hit-to-lead
optimization in drug discovery research, requires molecular representation. Previous reports …
optimization in drug discovery research, requires molecular representation. Previous reports …
Artificial intelligence to deep learning: machine intelligence approach for drug discovery
Drug designing and development is an important area of research for pharmaceutical
companies and chemical scientists. However, low efficacy, off-target delivery, time …
companies and chemical scientists. However, low efficacy, off-target delivery, time …
Artificial intelligence in small molecule drug discovery from 2018 to 2023: Does it really work?
Q Lv, F Zhou, X Liu, L Zhi - Bioorganic Chemistry, 2023 - Elsevier
Utilizing artificial intelligence (AI) in drug design represents an advanced approach for
identifying targets and developing new drugs. Integrating AI techniques significantly reduces …
identifying targets and developing new drugs. Integrating AI techniques significantly reduces …
A review on deep learning-driven drug discovery: strategies, tools and applications
It takes an average of 10-15 years to uncover and develop a new drug, and the process is
incredibly time-consuming, expensive, difficult, and ineffective. In recent years the dramatic …
incredibly time-consuming, expensive, difficult, and ineffective. In recent years the dramatic …
Current strategies to address data scarcity in artificial intelligence-based drug discovery: A comprehensive review
Artificial intelligence (AI) has played a vital role in computer-aided drug design (CADD). This
development has been further accelerated with the increasing use of machine learning (ML) …
development has been further accelerated with the increasing use of machine learning (ML) …
Applications of deep-learning in exploiting large-scale and heterogeneous compound data in industrial pharmaceutical research
In recent years, the development of high-throughput screening (HTS) technologies and their
establishment in an industrialized environment have given scientists the possibility to test …
establishment in an industrialized environment have given scientists the possibility to test …
A systematic review of deep learning methodologies used in the drug discovery process with emphasis on in vivo validation
NM Koutroumpa, KD Papavasileiou… - International Journal of …, 2023 - mdpi.com
The discovery and development of new drugs are extremely long and costly processes.
Recent progress in artificial intelligence has made a positive impact on the drug …
Recent progress in artificial intelligence has made a positive impact on the drug …
Machine learning and artificial intelligence: a paradigm shift in big data-driven drug design and discovery
P Pasrija, P Jha, P Upadhyaya… - Current Topics in …, 2022 - benthamdirect.com
Background: The lengthy and expensive process of developing a novel medicine often takes
many years and entails a significant financial burden due to its poor success rate …
many years and entails a significant financial burden due to its poor success rate …
Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling
Highlights•Drug discovery has been advanced to a big data era with a large amount of
public data sources available.•Ten V features (volume, velocity, variety, veracity, validity …
public data sources available.•Ten V features (volume, velocity, variety, veracity, validity …
Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries
C Selvaraj, I Chandra, SK Singh - Molecular diversity, 2021 - Springer
The global spread of COVID-19 has raised the importance of pharmaceutical drug
development as intractable and hot research. Developing new drug molecules to overcome …
development as intractable and hot research. Developing new drug molecules to overcome …
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