A Deep Learning-Based Quantitative Structure-Activity Relationship System Construct Prediction Model of Agonist and Antagonist with High Performance. [PDF]
Matsuzaka Y, Uesawa Y.
europepmc +1 more source
Porous silicon nanoparticles (PSiNPs) reprogram macrophage endocytosis of manganese@albumin‐based TLR4 nanoagonists, driving TRIF‐biased TLR4 signaling, eliciting robust proinflammatory responses, and potentiating macrophage‐mediated immunotherapeutic effects against NSCLC.
Xiaomei Zhang +9 more
wiley +1 more source
A Simple Machine Learning-Based Quantitative Structure-Activity Relationship Model for Predicting pIC50 Inhibition Values of FLT3 Tyrosine Kinase. [PDF]
Alcázar JJ +7 more
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The Psychonauts' Benzodiazepines; Quantitative Structure-Activity Relationship (QSAR) Analysis and Docking Prediction of Their Biological Activity. [PDF]
Catalani V +6 more
europepmc +1 more source
The versatile precursor‐assisted soft sphere close packing during slot‐die coating is investigated with in situ X‐ray scattering. The soft crystallization pathways towards a close packing involve multistep structural transitions such as surface nucleation, in‐plane, and out‐of‐plane crystallization.
Guangjiu Pan +14 more
wiley +1 more source
Three-dimensional Quantitative Structure-activity Relationship, Molecular Docking and Absorption, Distribution, Metabolism, and Excretion Studies of Lidocaine Analogs Pertaining to Voltage-gated Sodium Channel Na<sub>v</sub>1.7 Inhibition for the Management of Neuropathic Pain. [PDF]
Sharma S, Rana P, Dhingra N, Kaur T.
europepmc +1 more source
Development of a Quasi-Quantitative Structure-Activity Relationship Model for Prediction of the Immobilization Response of Daphnia magna Exposed to Metal-Based Nanomaterials. [PDF]
Bunmahotama W, Vijver MG, Peijnenburg W.
europepmc +1 more source

