Infrared Spectral Analysis for Prediction of Functional Groups Based on Feature-Aggregated Deep Learning

Infrared (IR) spectroscopy is a powerful and versatile tool for analyzing functional groups in organic compounds. A complex and time-consuming interpretation of massive unknown spectra usually requires knowledge of chemistry and spectroscopy. This paper presents a new deep learning method for transforming IR spectral features into intuitive imagelike feature maps and prediction of major functional groups. We obtained 8272 gas-phase IR spectra from the NIST Chemistry WebBook. Feature maps are constructed using the intrinsic correlation of spectral data, and prediction models are developed based on convolutional neural networks. Twenty-one major functional groups for each molecule are successfully identified using binary and multilabel models without expert guidance and feature selection. The multilabel classification model can produce all prediction results simultaneously for rapid characterization. Further analysis of the detailed substructures indicates that our model is capable of obtaining abundant structural information from IR spectra for a comprehensive investigation. The interpretation of our model reveals that the peaks of most interest are similar to those often considered by spectroscopists. In addition to demonstrating great potential for spectral identification, our method may contribute to the development of automated analyses in many fields

Virtual screening prediction of new potential organocatalysts for direct aldol reactions

A support vector machine (SVM)-based virtual screening method is demonstrated as a rapid computational tool for the prediction of potential asymmetric organocatalysts for the direct aldol reaction. Our models show good accuracy at cross-validation and independent testing. Structure analyses of screening hits from the PubChem database revealed several new classes of compounds, including beta-amino acids, diamines and hydrazides, as potential chiral organocatalysts.</p

Virtual screening prediction of new potential organocatalysts for direct aldol reactions

A support vector machine (SVM)-based virtual screening method is demonstrated as a rapid computational tool for the prediction of potential asymmetric organocatalysts for the direct aldol reaction. Our models show good accuracy at cross-validation and independent testing. Structure analyses of screening hits from the PubChem database revealed several new classes of compounds, including beta-amino acids, diamines and hydrazides, as potential chiral organocatalysts.</p

The potency improvement profile of the constituent NPs.

<p>The potency improvement profile of the constituent NPs.</p

Effect of Selection of Molecular Descriptors on the Prediction of Blood−Brain Barrier Penetrating and Nonpenetrating Agents by Statistical Learning Methods

The ability or inability of a drug to penetrate into the brain is a key consideration in drug design. Drugs for treating central nervous system (CNS) disorders need to be able to penetrate the blood−brain barrier (BBB). BBB nonpenetration is desirable for non-CNS-targeting drugs to minimize potential CNS-related side effects. Computational methods have been employed for the prediction of BBB-penetrating (BBB+) and -nonpenetrating (BBB−) agents at impressive accuracies of 75∼92% and 60∼80%, respectively. However, the majority of these studies give a substantially lower BBB− accuracy, and thus overall accuracy, than the BBB+ accuracy. This work examined whether proper selection of molecular descriptors can improve both the BBB− and the overall accuracies of statistical learning methods. The methods tested include logistic regression, linear discriminate analysis, k nearest neighbor, C4.5 decision tree, probabilistic neural network, and support vector machine. Molecular descriptors were selected by using a feature selection method, recursive feature elimination (RFE). Results by using 415 BBB+ and BBB− agents show that RFE substantially improves both the BBB− and the overall accuracy for all of the methods studied. This suggests that statistical learning methods combined with proper feature selection is potentially useful for facilitating a more balanced and improved prediction of BBB+ and BBB− agents

The targets and potency-enhancing synergistic molecular modes of the anticancer combination of Tetraarsenic tetrasulfide, Indirubin, and Tanshinone IIA (anticancer synergism reported).

<p>The detailed descriptions of the relevant molecular interaction profiles are in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049969#pone.0049969.s007" target="_blank">Table S7</a>.</p

Potency distribution profiles of 88 and 650 anticancer drugs and natural products.

<p>Potency distribution profiles of 88 and 650 anticancer drugs and natural products.</p

Potency distribution profiles of 102, 609 and 99 antimicrobial drugs, natural products (NPs) and NP extracts.

<p>Potency distribution profiles of 102, 609 and 99 antimicrobial drugs, natural products (NPs) and NP extracts.</p

Synergism level of 124 synergistic NP combinations.

<p>VSS, SS, S, MS, sS: very strong, strong, normal, moderate, slight synergism, NA: nearly additive, SA, MA: slight, moderate antagonism.</p

Expression profiles of the primary targets and some of the potency-enhancing secondary targets of the selected natural product combinations in specific patient groups.

<p>Expression profiles of the primary targets and some of the potency-enhancing secondary targets of the selected natural product combinations in specific patient groups.</p