Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions

S Vatansever, A Schlessinger, D Wacker… - Medicinal research …, 2021 - Wiley Online Library
Neurological disorders significantly outnumber diseases in other therapeutic areas.
However, develo** drugs for central nervous system (CNS) disorders remains the most …

Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system

V Gautam, A Gaurav, N Masand, VS Lee, VM Patil - Molecular Diversity, 2023 - Springer
CNS disorders are indications with a very high unmet medical needs, relatively smaller
number of available drugs, and a subpar satisfaction level among patients and caregiver …

Principles of ice-free cryopreservation by vitrification

GM Fahy, B Wowk - Cryopreservation and freeze-drying protocols, 2021 - Springer
Vitrification is an alternative to cryopreservation by freezing that enables hydrated living cells
to be cooled to cryogenic temperatures in the absence of ice. Vitrification simplifies and …

Hybridizing feature selection and feature learning approaches in QSAR modeling for drug discovery

I Ponzoni, V Sebastián-Pérez, C Requena-Triguero… - Scientific reports, 2017 - nature.com
Quantitative structure–activity relationship modeling using machine learning techniques
constitutes a complex computational problem, where the identification of the most …

Quantum artificial neural network approach to derive a highly predictive 3D-QSAR model for blood–brain barrier passage

T Kim, BH You, S Han, HC Shin, KC Chung… - International journal of …, 2021 - mdpi.com
A successful passage of the blood–brain barrier (BBB) is an essential prerequisite for the
drug molecules designed to act on the central nervous system. The logarithm of blood–brain …

Peptidic antifreeze materials: prospects and challenges

R Surís-Valls, IK Voets - International Journal of Molecular Sciences, 2019 - mdpi.com
Necessitated by the subzero temperatures and seasonal exposure to ice, various organisms
have developed a remarkably effective means to survive the harsh climate of their natural …

Derivation of Highly Predictive 3D-QSAR Models for hERG Channel Blockers Based on the Quantum Artificial Neural Network Algorithm

T Kim, KC Chung, H Park - Pharmaceuticals, 2023 - mdpi.com
The hERG potassium channel serves as an annexed target for drug discovery because the
associated off-target inhibitory activity may cause serious cardiotoxicity. Quantitative …

Data-driven discovery of potent small molecule ice recrystallisation inhibitors

MT Warren, CI Biggs, A Bissoyi, MI Gibson… - Nature …, 2024 - nature.com
Controlling the formation and growth of ice is essential to successfully cryopreserve cells,
tissues and biologics. Current efforts to identify materials capable of modulating ice growth …

Synthesis, biological evaluation and molecular docking study of N-(2-methoxyphenyl)-6-((4-nitrophenyl) sulfonyl) benzamide derivatives as potent HIV-1 Vif …

M Zhou, RH Luo, XY Hou, RR Wang, GY Yan… - European journal of …, 2017 - Elsevier
Viral infectivity factor (Vif) is protective against APOBEC3G (A3G)-mediated viral cDNA
hypermutations, and development of molecules that inhibit Vif mediated A3G degradation is …

Predicting the electrochemical properties of lithium-ion battery electrode materials with the quantum neural network algorithm

H Choi, KS Sohn, M Pyo, KC Chung… - The Journal of Physical …, 2019 - ACS Publications
Discovery of new inorganic solid materials can be accelerated with the aid of a reliable
computational tool for predicting the associated electrochemical properties. Hence, we …