[HTML][HTML] Advancement and prospects of bioinformatics analysis for studying bioactive peptides from food-derived protein: Sequence, structure, and functions
M Tu, S Cheng, W Lu, M Du - TrAC Trends in Analytical Chemistry, 2018 - Elsevier
Food-derived bioactive peptides, as potential ingredients in health-promoting functional
foods targeting diet-related chronic diseases, have attracted increasing attention because of …
foods targeting diet-related chronic diseases, have attracted increasing attention because of …
Quantitative correlation of physical and chemical properties with chemical structure: utility for prediction
AR Katritzky, M Kuanar, S Slavov, CD Hall… - Chemical …, 2010 - ACS Publications
All properties of organic moleculessphysical, chemical, biological, and
technologicalsdepend on their chemical structure and vary with it in a systematic way. The …
technologicalsdepend on their chemical structure and vary with it in a systematic way. The …
QSPR analysis of some novel drugs used in blood cancer treatment via degree based topological indices and regression models
Topological indices (TIs) have several biological applications in the treatment of blood
cancer. TIs can be used to predict the efficacy of drugs in cancer treatment by providing …
cancer. TIs can be used to predict the efficacy of drugs in cancer treatment by providing …
[图书][B] Handbook of solubility data for pharmaceuticals
A Jouyban - 2009 - taylorfrancis.com
Aqueous solubility is one of the major challenges in the early stages of drug discovery. One
of the most common and effective methods for enhancing solubility is the addition of an …
of the most common and effective methods for enhancing solubility is the addition of an …
Machine learning in prediction of intrinsic aqueous solubility of drug‐like compounds: Generalization, complexity, or predictive ability?
We present a collection of publicly available intrinsic aqueous solubility data of 829 drug‐
like compounds. Four different machine learning algorithms (random forests [RF], LightGBM …
like compounds. Four different machine learning algorithms (random forests [RF], LightGBM …
Recent advances on aqueous solubility prediction
Aqueous solubility is one of the major physiochemical properties to be optimized in drug
discovery. It is related to absorption and distribution in the ADME-Tox (Absorption …
discovery. It is related to absorption and distribution in the ADME-Tox (Absorption …
Synergy between machine learning and natural products cheminformatics: Application to the lead discovery of anthraquinone derivatives
Cheminformatics utilizing machine learning (ML) techniques have opened up a new horizon
in drug discovery. This is owing to vast chemical space expansion with rocketing numbers of …
in drug discovery. This is owing to vast chemical space expansion with rocketing numbers of …
QSAR on aryl-piperazine derivatives with activity on malaria
In this work we offer linear regression models on a set of aryl-piperazine derivatives that are
obtained by exploring a pool containing 1497 Dragon molecular descriptors, in order to …
obtained by exploring a pool containing 1497 Dragon molecular descriptors, in order to …
Will we ever be able to accurately predict solubility?
Accurate prediction of thermodynamic solubility by machine learning remains a challenge.
Recent models often display good performances, but their reliability may be deceiving when …
Recent models often display good performances, but their reliability may be deceiving when …
Magnetic oleosome as a functional lipophilic drug carrier for cancer therapy
In the present study, we fabricated magnetic oleosomes functionalized with recombinant
proteins as a new carrier for oil-based lipophilic drugs for cancer treatment. The …
proteins as a new carrier for oil-based lipophilic drugs for cancer treatment. The …