4 research outputs found
Prediction of Drug-Likeness Using Deep Autoencoder Neural Networks
Due to diverse reasons, most drug candidates cannot eventually become marketed drugs. Developing reliable computational methods for prediction of drug-likeness of candidate compounds is of vital importance to improve the success rate of drug discovery and development. In this study, we used a fully connected neural networks (FNN) to construct drug-likeness classification models with deep autoencoder to initialize model parameters. We collected datasets of drugs (represented by ZINC World Drug), bioactive molecules (represented by MDDR and WDI), and common molecules (represented by ZINC All Purchasable and ACD). Compounds were encoded with MOLD2 two-dimensional structure descriptors. The classification accuracies of drug-like/non-drug-like model are 91.04% on WDI/ACD databases, and 91.20% on MDDR/ZINC, respectively. The performance of the models outperforms previously reported models. In addition, we develop a drug/non-drug-like model (ZINC World Drug vs. ZINC All Purchasable), which distinguishes drugs and common compounds, with a classification accuracy of 96.99%. Our work shows that by using high-latitude molecular descriptors, we can apply deep learning technology to establish state-of-the-art drug-likeness prediction models
MolMiner: You only look once for chemical structure recognition
Molecular structures are always depicted as 2D printed form in scientific
documents like journal papers and patents. However, these 2D depictions are not
machine-readable. Due to a backlog of decades and an increasing amount of these
printed literature, there is a high demand for the translation of printed
depictions into machine-readable formats, which is known as Optical Chemical
Structure Recognition (OCSR). Most OCSR systems developed over the last three
decades follow a rule-based approach where the key step of vectorization of the
depiction is based on the interpretation of vectors and nodes as bonds and
atoms. Here, we present a practical software MolMiner, which is primarily built
up using deep neural networks originally developed for semantic segmentation
and object detection to recognize atom and bond elements from documents. These
recognized elements can be easily connected as a molecular graph with
distance-based construction algorithm. We carefully evaluate our software on
four benchmark datasets with the state-of-the-art performance. Various real
application scenarios are also tested, yielding satisfactory outcomes. The free
download links of Mac and Windows versions are available: Mac:
https://molminer-cdn.iipharma.cn/pharma-mind/artifact/latest/mac/PharmaMind-mac-latest-setup.dmg
and Windows:
https://molminer-cdn.iipharma.cn/pharma-mind/artifact/latest/win/PharmaMind-win-latest-setup.exeComment: 19 pages, 4 figure