Reducing false positive rate of docking-based virtual screening by active learning.

Machine learning-based scoring functions (MLSFs) have become a very favorable alternative to classical scoring functions because of their potential superior screening performance. However, the information of negative data used to construct MLSFs was rarely reported in the literature, and meanwhile the putative inactive molecules recorded in existing databases usually have obvious bias from active molecules. Here we proposed an easy-to-use method named AMLSF that combines active learning using negative molecular selection strategies with MLSF, which can iteratively improve the quality of inactive sets and thus reduce the false positive rate of virtual screening. We chose energy auxiliary terms learning as the MLSF and validated our method on eight targets in the diverse subset of DUD-E. For each target, we screened the IterBioScreen database by AMLSF and compared the screening results with those of the four control models. The results illustrate that the number of active molecules in the top 1000 molecules identified by AMLSF was significantly higher than those identified by the control models. In addition, the free energy calculation results for the top 10 molecules screened out by the AMLSF, null model and control models based on DUD-E also proved that more active molecules can be identified, and the false positive rate can be reduced by AMLSF.

Relationship Between Temporomandibular Joint Effusion, Pain, and Jaw Function Limitation: A 2D and 3D Comparative Study.

Purpose: This study aimed to investigate the relationship between temporomandibular joint (TMJ) effusion and TMJ pain, as well as jaw function limitation in patients via two-dimensional (2D) and three-dimensional (3D) magnetic resonance imaging (MRI) evaluation. Patients and Methods: 121 patients diagnosed with temporomandibular disorder (TMD) were included. TMJ effusion was assessed qualitatively using MRI and quantified with 3D Slicer software, then graded accordingly. In addition, a visual analogue scale (VAS) was employed for pain reporting and an 8-item Jaw Functional Limitations Scale (JFLS-8) was utilized to evaluate jaw function limitation. Statistical analyses were performed appropriately for group comparisons and association determination. A probability of p<0.05 was considered statistically significant. Results: 2D qualitative and 3D quantitative strategies were in high agreement for TMJ effusion grades (κ = 0.766). No significant associations were found between joint effusion and TMJ pain, nor with disc displacement and JLFS-8 scores. Moreover, the binary logistic regression analysis showed significant association between sex and the presence of TMJ effusion, exhibiting an Odds Ratio of 5.168 for females (p = 0.008). Conclusion: 2D qualitative evaluation was as effective as 3D quantitative assessment for TMJ effusion diagnosis. No significant associations were found between TMJ effusion and TMJ pain, disc displacement or jaw function limitation. However, it was suggested that female patients suffering from TMD may be at a risk for TMJ effusion. Further prospective research is needed for validation.