A multimodal DTA prediction method based on triple-view contrastive learning
Drug-target affinity (DTA) prediction is a crucial step in drug discovery, facilitating the acceleration of lead compound screening and drug repurposing, significantly reducing costs and shortening new drug development timelines.
Xiaoxing Pang +5 more
doaj +2 more sources
A deep learning method for drug-target affinity prediction based on sequence interaction information mining [PDF]
Background A critical aspect of in silico drug discovery involves the prediction of drug-target affinity (DTA). Conducting wet lab experiments to determine affinity is both expensive and time-consuming, making it necessary to find alternative approaches.
Mingjian Jiang +4 more
doaj +2 more sources
The Effect of Manganese on the Crystallisation Process, Microstructure and Selected Properties of Compacted Graphite iron [PDF]
The paper presents the effect of manganese on the crystallization process, microstructure and selected properties: cast iron hardness as well as ferrite and pearlite microhardness. The compacted graphite was obtained by Inmold technology.
G. Gumienny, B. Kurowska, P. Just
doaj +1 more source
BackgroundThe novel DiamondTemp ablation system (DTA) and EnSiteX mapping System (EAM) are both CE-Marked and FDA approved medical devices. The DTA has been validated by its manufacturer only in combination with previous version of EnSite System—EnSite ...
Luigi Pannone +7 more
doaj +1 more source
Case-based knowledge formalization and reasoning method for digital terrain analysis – application to extracting drainage networks [PDF]
Application of digital terrain analysis (DTA), which is typically a modeling process involving workflow building, relies heavily on DTA domain knowledge of the match between the algorithm (and its parameter settings) and the application context ...
C.-Z. Qin +3 more
doaj +1 more source
Background Drug-target binding affinity (DTA) prediction is important for the rapid development of drug discovery. Compared to traditional methods, deep learning methods provide a new way for DTA prediction to achieve good performance without much ...
Leiming Xia +5 more
doaj +1 more source
A Site-Aware Representation Learning Framework For Unified Molecular Interaction Modeling and Generative Design. [PDF]
MolDBG is a site‐aware, sequence‐only framework that unifies drug‐target affinity prediction, binding‐site identification, and affinity‐conditioned molecular generation for structured proteins. Guided by multi‐task binding‐site supervision, it aligns interaction‐critical residues before learning drug‐target representations and simultaneously infers ...
Luo G +6 more
europepmc +2 more sources
Associative learning mechanism for drug‐target interaction prediction
As a necessary process of modern drug development, finding a drug compound that can selectively bind to a specific protein is highly challenging and costly.
Zhiqin Zhu +5 more
doaj +1 more source
Comparison of two methods to calculate external loads at flight in continuous turbulence
The external loads from the continuous turbulence on the elastic high aspect ratio wing of the transport category aircraft are calculated by Dynamics of Turbulence Air (DTA) and Interactive Multidisciplinary Aircraft Design (IMAD) methods.
Bohdan Hevko, Yurij Bondar
doaj +1 more source
Crystallization Kinetics of Fractionated Polyethylenes at High Pressures by a DTA Method [PDF]
The crystallization kinetics of fractionated polyethylenes under high pressure was studied by the DTA method for samples having molecular weights of 15500 and 150000. In extended-chain crystallization there was a saturation phenomenon on producing extended-chain crystals. The amount of saturation was dependent on the crystallization temperature.
Sawada, Shuichi, Nose, Takuhei
openaire +1 more source

