FormulationBCS: A Machine Learning Platform Based on Diverse Molecular Representations for Biopharmaceutical Classification System (BCS) Class Prediction.

The Biopharmaceutics Classification System (BCS) has facilitated biowaivers and played a significant role in enhancing drug regulation and development efficiency. However, the productivity of measuring the key discriminative properties of BCS, solubility and permeability, still requires improvement, limiting high-throughput applications of BCS, which is essential for evaluating drug candidate developability and guiding formulation decisions in the early stages of drug development. In recent years, advancements in machine learning (ML) and molecular characterization have revealed the potential of quantitative structure-performance relationships (QSPR) for rapid and accurate BCS classification.

FormulationAI: a novel web-based platform for drug formulation design driven by artificial intelligence.

Today, pharmaceutical industry faces great pressure to employ more efficient and systematic ways in drug discovery and development process. However, conventional formulation studies still strongly rely on personal experiences by trial-and-error experiments, resulting in a labor-consuming, tedious and costly pipeline. Thus, it is highly required to develop intelligent and efficient methods for formulation development to keep pace with the progress of the pharmaceutical industry.

Promoted removal of phosphate by layered double hydroxides combined with bacteria: Application of novel carriers in biofilm reactor.

Layered double hydroxides (LDHs) were used as carriers for the microbial consortium in sequencing biofilm batch reactor (SBBR) without inoculation to promote the removal of phosphate. The adsorption capacity of [Zn-Al]-LDH was significantly better than that of [Mg-Al]-LDH. The pollutants removal performance and behavior of microorganisms in LDH-SBBRs were also investigated.

Robust and Fast Decoding of High-Capacity Color QR Codes for Mobile Applications.

The use of color in QR codes brings extra data capacity, but also inflicts tremendous challenges on the decoding process due to chromatic distortion-cross-channel color interference and illumination variation. Particularly, we further discover a new type of chromatic distortion in high-density color QR codes-cross-module color interference-caused by the high density which also makes the geometric distortion correction more challenging. To address these problems, we propose two approaches, LSVM-CMI and QDA-CMI, which jointly model these different types of chromatic distortion.