Pruned Machine Learning Models to Predict Aqueous Solubility
نویسندگان
چکیده
منابع مشابه
Random Forest Models To Predict Aqueous Solubility
Random Forest regression (RF), Partial-Least-Squares (PLS) regression, Support Vector Machines (SVM), and Artificial Neural Networks (ANN) were used to develop QSPR models for the prediction of aqueous solubility, based on experimental data for 988 organic molecules. The Random Forest regression model predicted aqueous solubility more accurately than those created by PLS, SVM, and ANN and offer...
متن کاملUsing Machine Learning ARIMA to Predict the Price of Cryptocurrencies
The increasing volatility in pricing and growing potential for profit in digital currency have made predicting the price of cryptocurrency a very attractive research topic. Several studies have already been conducted using various machine-learning models to predict crypto currency prices. This study presented in this paper applied a classic Autoregressive Integrated Moving Average(ARIMA) model ...
متن کاملFindings of the Challenge To Predict Aqueous Solubility
The Solubility Challenge is based upon intrinsic solubility data measured in one laboratory for a set of biologically relevant compounds. More than 100 entries to the Solubility Challenge have been received. In several cases multiple entries came from the same person or group. In addition, more than 5% of the prediction sheet entries were incomplete in that predictions were not reported for all...
متن کاملA simple QSPR model to predict aqueous solubility of drugs
Aqueous solubility of a drug/drug candidate is essential data in drug discovery, and an in silico method for predicting the aqueous solubility of drug candidates provides a valuable tool to speed up the process of drug discovery and development. This paper describes a simple quantitative structure property relationship (QSPR) model for predicting the aqueous solubility of drugs which is validat...
متن کاملApplying Different Machine Learning Models to Predict Breast Cancer Risk
In this paper, we apply five machine learning models (Logistic Regression, Naive Bayes, LinearSVC, SVM with linear kernel and Random Forest) and three feature selection techniques (PCA, RFE and Heatmap) in one of the key procedures for breast cancer diagnosis. Using the biopsy cytopathology data with 30 numerical features, we achieve a high accuracy of 97.8%. We further compare performances of ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: ACS Omega
سال: 2020
ISSN: 2470-1343,2470-1343
DOI: 10.1021/acsomega.0c01251