With the diversification of diseases and the frequent emergence of drug resistance issues in recent years, the demand for drugs has increased day by day. However, the development of new drugs has always been characterized by long development cycles, high costs and low success rates. The development of artificial intelligence is expected to shorten the time for drug development significantly and increase the success rate of drug development. Furthermore, artificial intelligence can participate in multiple stages of drug development, such as drug-target prediction, drug interaction prediction, drug toxicology analysis, drug repositioning, the impact of drugs on microorganisms, etc. Therefore, the development of efficient artificial intelligence methods is of great significance for drug-related research.
Based on information such as drug structure, drug targets, drug interactions, drug- microbe, disease semantics, and disease-proteins, design a suitable computational method to predict potential therapeutic values of drugs. It is also possible to perform multi-target analysis on drugs to determine whether drug targets play a regulatory role in disease pathways. We also welcome the use of artificial intelligence methods for other related research, such as drug-target prediction, drug interactions analysis, drug-microbe prediction, and drug toxicology prediction. It is also of great significance to clarify their roles in drug repositioning research. In method design, we encourage contributors to use the latest artificial intelligence methods, including machine learning, deep learning, statistics, intelligent optimization and other models, and to be able to verify and explain their research results reasonably.
The scope of research topics includes but is not limited to artificial intelligence-based drug repositioning, drug-target prediction, drug interaction analysis, drug toxicology prediction, drug-microbe prediction and drug discovery. We welcome submissions of the following article types: Hypothesis and Theory, Methods, Mini Review, Opinion, Original Research, Perspective, Review, Systematic Review and Technology and Code.
Important note for Authors: All manuscripts submitted to this collection will need to follow the Guidelines for the conception/peer-review of submissions of the Experimental Pharmacology and Drug Discovery Section. Studies based solely on in silico techniques will not be considered for review.
With the diversification of diseases and the frequent emergence of drug resistance issues in recent years, the demand for drugs has increased day by day. However, the development of new drugs has always been characterized by long development cycles, high costs and low success rates. The development of artificial intelligence is expected to shorten the time for drug development significantly and increase the success rate of drug development. Furthermore, artificial intelligence can participate in multiple stages of drug development, such as drug-target prediction, drug interaction prediction, drug toxicology analysis, drug repositioning, the impact of drugs on microorganisms, etc. Therefore, the development of efficient artificial intelligence methods is of great significance for drug-related research.
Based on information such as drug structure, drug targets, drug interactions, drug- microbe, disease semantics, and disease-proteins, design a suitable computational method to predict potential therapeutic values of drugs. It is also possible to perform multi-target analysis on drugs to determine whether drug targets play a regulatory role in disease pathways. We also welcome the use of artificial intelligence methods for other related research, such as drug-target prediction, drug interactions analysis, drug-microbe prediction, and drug toxicology prediction. It is also of great significance to clarify their roles in drug repositioning research. In method design, we encourage contributors to use the latest artificial intelligence methods, including machine learning, deep learning, statistics, intelligent optimization and other models, and to be able to verify and explain their research results reasonably.
The scope of research topics includes but is not limited to artificial intelligence-based drug repositioning, drug-target prediction, drug interaction analysis, drug toxicology prediction, drug-microbe prediction and drug discovery. We welcome submissions of the following article types: Hypothesis and Theory, Methods, Mini Review, Opinion, Original Research, Perspective, Review, Systematic Review and Technology and Code.
Important note for Authors: All manuscripts submitted to this collection will need to follow the Guidelines for the conception/peer-review of submissions of the Experimental Pharmacology and Drug Discovery Section. Studies based solely on in silico techniques will not be considered for review.
