OCMR: A comprehensive framework for optical chemical molecular recognition.

Artificial intelligence (AI) has achieved significant progress in the field of drug discovery. AI-based tools have been used in all aspects of drug discovery, including chemical structure recognition. We propose a chemical structure recognition framework, Optical Chemical Molecular Recognition (OCMR), to improve the data extraction capability in practical scenarios compared with the rule-based and end-to-end deep learning models.

Tensile Behaviors and Strain Hardening Mechanisms in a High-Mn Steel with Heterogeneous Microstructure.

Heterogeneous structures with both heterogeneous grain structure and dual phases have been designed and obtained in a high-Mn microband-induced plasticity (MBIP) steel. The heterogeneous structures show better synergy of strength and ductility as compared to the homogeneous structures. Higher contribution of hetero-deformation induced hardening to the overall strain hardening was observed and higher density of geometrically necessary dislocations were found to be induced at various domain boundaries in the heterogeneous structures, resulting in higher extra strain hardening for the observed better tensile properties as compared to the homogeneous structures.

Fingerprint-based computational models of 5-lipo-oxygenase activating protein inhibitors: Activity prediction and structure clustering.

Inflammatory diseases can be treated by inhibiting 5-lipo-oxygenase activating protein (FLAP). In this study, a data set containing 2,112 FLAP inhibitors was collected. A total of 25 classification models were built by five machine learning algorithms with five different types of fingerprints.

Classification of Cyclooxygenase-2 Inhibitors Using Support Vector Machine and Random Forest Methods.

This work reports the classification study conducted on the biggest COX-2 inhibitor data set so far. Using 2925 diverse COX-2 inhibitors collected from 168 pieces of literature, we applied machine learning methods, support vector machine (SVM) and random forest (RF), to develop 12 classification models. The best SVM and RF models resulted in MCC values of 0.

SAR study on inhibitors of GIIA secreted phospholipase A using machine learning methods.

GIIA secreted phospholipase A (GIIA sPLA ) is a potent target for drug discovery. To distinguish the activity level of the inhibitors of GIIA sPLA , we built 24 classification models by three machine learning algorithms including support vector machine (SVM), decision tree (DT), and random forest (RF) based on 452 compounds. The molecules were represented by CORINA descriptors, MACCS fingerprints, and ECFP4 fingerprints, respectively.