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Purpose: To establish consensus among experts in lens and refractive surgery to guide general ophthalmologists on issues related to presbyopia-correcting intraocular lenses (IOLs)., Design: A modified Delphi method to reach a consensus among experts., Methods: A steering committee formulated 105 relevant items grouped into four sections (preoperative considerations, IOL selection, intraoperative considerations, and postoperative considerations). The consensus was defined as ≥ 70% of experts agreeing with the evaluation of a statement., Results: Ten experts participated and completed all rounds of questionnaires (100% response rate). Of 68 items considered in the preoperative considerations, consensus was achieved in 48 (70.6%). There was a lack of consensus over IOL selection, the experts only agreed on the importance of the patient's habits for the optical IOL design selection. Of the 14 considerations related to intraoperative issues, the experts reached a consensus on 10 (71.4%). The postoperative considerations section reached the highest consensus in 10 items of 13 (76.9%)., Conclusions: Key recommendations for a diffractive multifocal IOL were a potential postoperative visual acuity > 0.5, a keratometry between 40-45 diopters, a pupil >2.8 mm under photopic conditions and <6.0 mm under scotopic conditions, a root mean square of higher order corneal aberrations <0.5 µm for 6-mm pupil size, while monofocal or non-diffractive IOLs should be considered for patients with coexisting eye disorders. A lack of agreement was found in the issues related to the IOL selection., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

AlphaScreen is one of the most widely used assay technologies in drug discovery due to its versatility, dynamic range and sensitivity. However, a presence of false positives and frequent hitters contributes to difficulties with an interpretation of measured HTS data. Although filters do exist to identify frequent hitters for AlphaScreen, they are frequently based on privileged scaffolds. The development of such filters is time consuming and requires deep domain knowledge. Recently, machine learning and artificial intelligence methods are emerging as important tools to advance drug discovery and chemoinformatics, including their application to identification of frequent hitters in screening assays. However, the relative performance and complementarity of the Machine Learning and scaffold-based techniques has not yet been comprehensively compared. In this study, we analysed filters based on the privileged scaffolds with filters built using machine learning. Our results demonstrate that machine-learning methods provide more accurate filters for identification of frequent hitters in AlphaScreen assays than scaffold-based methods and can be easily redeveloped once new data are measured. We present highly accurate models to identify frequent hitters in AlphaScreen assays., (© 2021 Wiley-VCH GmbH.)