Review
Pharmacology & Pharmacy
Zhichao Liu, Ruth A. Roberts, Madhu Lal-Nag, Xi Chen, Ruili Huang, Weida Tong
Summary: The use of AI-powered LMs has revolutionized the landscape of natural language processing in drug discovery and development, especially in research on COVID-19 treatment strategies. AI-powered LMs have great potential in target identification, clinical design, regulatory decision making, and pharmacovigilance.
DRUG DISCOVERY TODAY
(2021)
Article
Chemistry, Medicinal
Sarfaraz K. Niazi
Summary: Artificial intelligence (AI) and machine learning (ML) have become crucial tools in drug discovery and manufacturing, with the FDA and other U.S. federal agencies issuing guidelines to ensure their safe and effective use. These technologies have the potential to expedite drug discovery, improve drug safety, and enable innovative applications in emerging therapies. Recent FDA publications highlight the need for careful deployment of AI/ML tools and the expanding market opportunities in training personnel to handle these technologies.
DRUG DESIGN DEVELOPMENT AND THERAPY
(2023)
Article
Biochemistry & Molecular Biology
Qi Lv, Feilong Zhou, Xinhua Liu, Liping Zhi
Summary: This review provides an overview of the utilization of artificial intelligence in drug design, focusing on protein structure prediction, molecular virtual screening, molecular design, and ADMET prediction. The role and limitations of AI in drug development are discussed, along with its impact on decision-making processes.
BIOORGANIC CHEMISTRY
(2023)
Article
Oncology
Ganggang Bai, Chuance Sun, Ziang Guo, Yangjing Wang, Xincheng Zeng, Yuhong Su, Qi Zhao, Buyong Ma
Summary: Therapeutic antibodies are a successful treatment for human diseases, but their design and discovery are challenging and time-consuming. Artificial intelligence has made significant advancements in these areas, particularly in computational predictors of antibody structure and developability. Machine learning offers new possibilities for fully computational antibody design.
SEMINARS IN CANCER BIOLOGY
(2023)
Article
Biochemical Research Methods
Vishakha Singh, Sameer Shrivastava, Sanjay Kumar Singh, Abhinav Kumar, Sonal Saxena
Summary: Utilizing a temporal convolutional network-based binary classification approach, we have proposed a method to discover new antifungal molecules to accelerate the development of antifungal medications; furthermore, we employed transfer learning technique to pre-train our model on antibacterial peptides, resulting in a classifier that predicts AFPs with an accuracy and precision of 94%.
BRIEFINGS IN BIOINFORMATICS
(2022)
Editorial Material
Cell Biology
Natalia Moskal, G. Angus McQuibban
Summary: We used artificial intelligence to simplify the small molecule drug screening process and discovered the cholesterol-reducing compound probucol. Probucol enhanced mitophagy and protected dopaminergic neurons from mitochondrial toxins in flies and zebrafish. Further investigation revealed that probucol modulates mitophagy through its target protein ABCA1, which is required for the regulation of lipid droplet dynamics during mitophagy. In this article, we will summarize the combination of in silico and cell-based screening that led us to identify and characterize probucol as a compound that enhances mitophagy, and discuss future directions for research in this area.
Review
Biochemistry & Molecular Biology
Chayna Sarkar, Biswadeep Das, Vikram Singh Rawat, Julie Birdie Wahlang, Arvind Nongpiur, Iadarilang Tiewsoh, Nari M. Lyngdoh, Debasmita Das, Manjunath Bidarolli, Hannah Theresa Sony
Summary: The discovery and advances of medicines are important in enhancing human invulnerability and happiness. However, the process of developing new drugs is complex, expensive, and time-consuming. The use of Artificial Intelligence (AI), especially deep learning (DL), and big data has helped accelerate and reduce costs in drug discovery. Machine learning algorithms, particularly DL methods, such as artificial neural networks, have shown great potential in automatic feature extraction. AI is expected to revolutionize the pharmaceutical industry by enabling faster and more efficient drug discovery.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Review
Chemistry, Multidisciplinary
Yannick Djoumbou-Feunang, Jeremy Wilmot, John Kinney, Pritam Chanda, Pulan Yu, Avery Sader, Max Sharifi, Scott Smith, Junjun Ou, Jie Hu, Elizabeth Shipp, Dirk Tomandl, Siva P. Kumpatla
Summary: The global cost-benefit analysis of pesticide use has shown a significant increase from 1990 to 2007 followed by a decline. Factors such as pest resistance, lack of innovation, and regulatory action have contributed to this trend. With the increasing global population, there will be a higher demand for food and thus the usage of pesticides will also increase. Therefore, there is a need to develop infrastructures for the discovery and development of novel and sustainable molecules to address these challenges.
FRONTIERS IN CHEMISTRY
(2023)
Review
Pharmacology & Pharmacy
Isabeau Vermeulen, Emre M. Isin, Patrick Barton, Berta Cillero-Pastor, Ron M. A. Heeren
Summary: In addition to individual imaging techniques, the combination and integration of several imaging techniques, known as multi-modal imaging, play a crucial role in drug discovery and development. They provide valuable information for understanding disease mechanisms, identifying new pharmacological targets, and evaluating potential drug candidates and treatment response.
DRUG DISCOVERY TODAY
(2022)
Review
Immunology
Jose T. Moreira-Filho, Arthur C. Silva, Rafael F. Dantas, Barbara F. Gomes, Lauro R. Souza Neto, Jose Brandao-Neto, Raymond J. Owens, Nicholas Furnham, Bruno J. Neves, Floriano P. Silva-Junior, Carolina H. Andrade
Summary: This review focuses on innovative approaches to identify antischistosomal drug candidates, including the use of automated assays, fragment-based screening, computer-aided, and artificial intelligence-based computational methods. Current developments may contribute to optimizing research outputs and lead to more effective drugs for this highly prevalent disease in a more cost-effective drug discovery endeavor.
FRONTIERS IN IMMUNOLOGY
(2021)
Review
Computer Science, Artificial Intelligence
Heba Askr, Enas Elgeldawi, Heba Aboul Ella, Yaseen A. M. M. Elshaier, Mamdouh M. Gomaa, Aboul Ella Hassanien
Summary: Recently, there has been a lot of attention on using artificial intelligence (AI) in drug discovery due to its ability to significantly reduce the time and cost of developing new drugs. This paper presents a systematic literature review (SLR) that integrates recent advancements in deep learning (DL) technology and its applications in various stages of drug development. The review covers topics such as drug-target interactions (DTIs), drug-drug similarity interactions (DDIs), drug sensitivity and responsiveness, and drug-side effect predictions. The paper also provides an overview of explainable AI (XAI) in supporting drug discovery problems, discusses drug dosing optimization and success stories, and proposes digital twining (DT) and open issues as future research challenges. Challenges to be addressed and future research directions are identified, and an extensive bibliography is included.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Review
Pharmacology & Pharmacy
R. S. K. Vijayan, Jan Kihlberg, Jason B. Cross, Vasanthanathan Poongavanam
Summary: Artificial intelligence is playing a crucial role in drug discovery, from target identification to preclinical development. This review provides an overview of current AI technologies and presents real impact examples, while discussing the opportunities and challenges of adopting AI in drug discovery.
DRUG DISCOVERY TODAY
(2022)
Review
Biochemical Research Methods
Wiktoria Wilman, Sonia Wrobel, Weronika Bielska, Piotr Deszynski, Pawel Dudzic, Igor Jaszczyszyn, Jedrzej Kaniewski, Jakub Mlokosiewicz, Anahita Rouyan, Tadeusz Satlawa, Sandeep Kumar, Victor Greiff, Konrad Krawczyk
Summary: Antibodies play a crucial role in therapeutics, and computational methods, including machine learning, are increasingly being used for antibody design and optimization. These methods offer improvements in structure prediction, antibody repertoire modeling, and generation of novel sequences.
BRIEFINGS IN BIOINFORMATICS
(2022)
Review
Biochemical Research Methods
Jianyuan Deng, Zhibo Yang, Iwao Ojima, Dimitris Samaras, Fusheng Wang
Summary: Artificial intelligence (AI) has played a crucial role in drug discovery over the past decade, with applications ranging from virtual screening to drug design. This survey provides a comprehensive overview of AI in drug discovery, covering tasks, data resources, model architectures, and learning paradigms.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Chemistry, Medicinal
Alexander K. Hurben, Luke Erber
Summary: This article provides a broad overview of AI research in the field of chemical toxicology, focusing on its applications in drug design, development, and safety assessment.
CHEMICAL RESEARCH IN TOXICOLOGY
(2022)
