Consensus holistic virtual screening for drug discovery: a novel machine learning model approach.

In drug discovery, virtual screening is crucial for identifying potential hit compounds. This study aims to present a novel pipeline that employs machine learning models that amalgamates various conventional screening methods. A diverse array of protein targets was selected, and their corresponding datasets were subjected to active/decoy distribution analysis prior to scoring using four distinct methods: QSAR, Pharmacophore, docking, and 2D shape similarity, which were ultimately integrated into a single consensus score. The fine-tuned machine learning models were ranked using the novel formula "w_new", consensus scores were calculated, and an enrichment study was performed for each target. Distinctively, consensus scoring outperformed other methods in specific protein targets such as PPARG and DPP4, achieving AUC values of 0.90 and 0.84, respectively. Remarkably, this approach consistently prioritized compounds with higher experimental PIC50 values compared to all other screening methodologies. Moreover, the models demonstrated a range of moderate to high performance in terms of R2 values during external validation. In conclusion, this novel workflow consistently delivered superior results, emphasizing the significance of a holistic approach in drug discovery, where both quantitative metrics and active enrichment play pivotal roles in identifying the best virtual screening methodology.Scientific contributionWe presented a novel consensus scoring workflow in virtual screening, merging diverse methods for enhanced compound selection. We also introduced 'w_new', a groundbreaking metric that intricately refines machine learning model rankings by weighing various model-specific parameters, revolutionizing their efficacy in drug discovery in addition to other domains.

Active incremental Support Vector Machine for oil and gas pipeline defects prediction system using long range ultrasonic transducers.

This work proposes a long range ultrasonic transducers technique in conjunction with an active incremental Support Vector Machine (SVM) classification approach that is used for real-time pipeline defects prediction and condition monitoring. Oil and gas pipeline defects are detected using various techniques. One of the most prevalent techniques is the use of "smart pigs" to travel along the pipeline and detect defects using various types of sensors such as magnetic sensors and eddy-current sensors. A critical short coming of "smart pigs" is the inability to monitor continuously and predict the onset of defects. The emergence of permanently installed long range ultrasonics transducers systems enable continuous monitoring to be achieved. The needs for and the challenges of the proposed technique are presented. The experimental results show that the proposed technique achieves comparable classification accuracy as when batch training is used, while the computational time is decreased, using 56 feature data points acquired from a lab-scale pipeline defect generating experimental rig.