Results 61 to 70 of about 6,006,073 (362)
m6A modification: recent advances, anticancer targeted drug discovery and beyond
Molecular Cancer, 2022 Abnormal N6-methyladenosine (m6A) modification is closely associated with the occurrence, development, progression and prognosis of cancer, and aberrant m6A regulators have been identified as novel anticancer drug targets.
Lijuan Deng +10 more
semanticscholar +1 more source
Practical considerations for active machine learning in drug discovery [PDF]
, 2020 Active machine learning enables the automated selection of the most valuable next experiments to improve predictive modelling and hasten active retrieval in drug discovery.
Reker, Daniel
core +1 more source
G protein-coupled receptors (GPCRs): advances in structures, mechanisms and drug discovery
Signal Transduction and Targeted TherapyG protein-coupled receptors (GPCRs), the largest family of human membrane proteins and an important class of drug targets, play a role in maintaining numerous physiological processes.
Mingyang Zhang +5 more
semanticscholar +1 more source
Dual Use of Artificial Intelligence-powered Drug Discovery
Nature Machine Intelligence, 2022 An international security conference explored how artificial intelligence (AI) technologies for drug discovery could be misused for de novo design of biochemical weapons. A thought experiment evolved into a computational proof.
Fabio Urbina +3 more
semanticscholar +1 more source
Anticancer Drug Discovery [PDF]
JNCI Journal of the National Cancer Institute, 1991 The National Cancer Institute's (NCI's) human tumor cell line panel for screening potential new anticancer drugs is now operational (/). Its implementation and the demonstration of its feasibility represent a technical and organizational tour de force.
openaire +2 more sources
Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Acute lymphoblastic leukemia (ALL) is the most common pediatric cancer, with an overall survival now surpassing 90% in developed countries. However, treatments are not without adverse effects. In this study, we apply the severe toxicity‐free survival (STFS) framework to determine the prevalence of 21 physician‐defined severe ...
Lane Collier +10 more
wiley +1 more source
Genetic programming in data mining for drug discovery [PDF]
, 2004 Genetic programming (GP) is used to extract from rat oral bioavailability (OB) measurements simple, interpretable and predictive QSAR models which both generalise to rats and to marketed drugs in humans. Receiver Operating Characteristics (ROC) curves
Barrett, S.J., Langdon, W.B.
core
Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Type 1 plasminogen deficiency (PLGD‐1) is an ultra‐rare autosomal recessive disorder caused by variants in the PLG gene and affects approximately 1.6 individuals per million. The condition is characterized by decreased plasminogen levels and impaired function, resulting in fibrin‐rich lesions on mucous membranes throughout the body.
Charles Nakar +7 more
wiley +1 more source
Deep learning in drug discovery: an integrative review and future challenges
Artificial Intelligence Review, 2022 Recently, using artificial intelligence (AI) in drug discovery has received much attention since it significantly shortens the time and cost of developing new drugs.
Heba Askr +5 more
semanticscholar +1 more source
MedChemComm, 2019 Exploiting glycan recognition in drug discovery.
Pablo Valverde +4 more
openaire +4 more sources