Simplified Molecular Input-line System Research Articles

Dielectric Polymer Property Prediction Using Recurrent Neural Networks with Optimizations.

Despite the growing success of machine learning for predicting structure-property relationships in molecules and materials, such as predicting the dielectric properties of polymers, it is still in its infancy. We report on the effectiveness of solving structure-property relationships for a computer-generated database of dielectric polymers using recurrent neural network (RNN) models. The implementation of a series of optimization strategies was crucial to achieving high learning speeds and sufficient accuracy: (1) binary and nonbinary representations of SMILES (Simplified Molecular Input Line System) fingerprints and (2) backpropagation with affine transformation of the input sequence (ATransformedBP) and resilient backpropagation with initial weight update parameter optimizations (iRPROP- optimized). For the investigated database of polymers, the binary SMILES representation was found to be superior to the decimal representation with respect to the training and prediction performance. All developed and optimized Elman-type RNN algorithms outperformed nonoptimized RNN models in the efficient prediction of nonlinear structure-activity relationships. The average relative standard deviation (RSD) remained well below 5%, and the maximum RSD did not exceed 30%. Moreover, we provide a C++ codebase as a testbed for a new generation of open programming languages that target increasingly diverse computer architectures.

LINGO-DL: a text-based approach for molecular similarity searching.

The line notations of chemical structures are more compact than those of graphs and connection tables, so they can be useful for storing and transferring a large number of molecular structures. The simplified molecular input line system (SMILES) representation is the most extensively used, as it is much easier to utilise and comprehend than others, and it can be generated automatically from connection tables. A SMILES represents and encodes the molecule structure. It has been used by an existing method, LINGO, to calculate the molecular similarities and predict the structure-related properties. The LINGO method decomposes a canonical SMILES into a set of substrings of four characters referred to as LINGOs. The purpose of LINGO method is to measure the similarity between a pair of molecules by comparing the LINGOs that occur in each molecule. This paper aims to introduce an alternative version of the LINGO method using LINGOs of different lengths, called LINGO-DL. LINGO-DL is based on the fragmentation of canonical SMILES into substrings of three different lengths rather than one in LINGO method. Retrospective virtual screening experiments with MDDR, DUD, and MUV datasets show that the LINGO-DL outperforms the LINGO method, especially when the active molecules being sought have a high degree of structural heterogeneity.

Artificial Neural Networks in ADMET Modeling: Prediction of Blood–Brain Barrier Permeation

AbstractA supervised artificial neural network (ANN) model has been developed for the accurate prediction of the Blood–Brain Barrier (BBB) partition (in Log BB scale) of chemical compounds. A structural diverse set of 108 compounds of known experimental Log BB value was chosen for this study. The molecules were defined by means of a non‐supervised neural network using our CODES program. This program codifies each molecule into a set of numerical parameters taking into account exclusively the information of its chemical structure from its Simplified Molecular Input Line System (SMILES) code. The model obtained averages 83% of accuracy in the training set and of 73% in the external prediction set. The model is able to predict correctly the behavior of a very heterogeneous series of compounds in terms of the BBB permeation. The results indicate that this approach may represent a useful tool for the prediction of Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) properties. CODES© is available free of charge for academic institutions.