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A crucial component in structure-based drug discovery is the availability of high-quality three-dimensional structures of the protein target. Whenever experimental structures were not available, homology modeling has been, so far, the method of choice. Recently, AlphaFold (AF), an artificial-intelligence-based protein structure prediction method, has shown impressive results in terms of model accuracy. This outstanding success prompted us to evaluate how accurate AF models are from the perspective of docking-based drug discovery. We compared the high-throughput docking (HTD) performance of AF models with their corresponding experimental PDB structures using a benchmark set of 22 targets. The AF models showed consistently worse performance using four docking programs and two consensus techniques. Although AlphaFold shows a remarkable ability to predict protein architecture, this might not be enough to guarantee that AF models can be reliably used for HTD, and post-modeling refinement strategies might be key to increase the chances of success., Competing Interests: The authors declare no competing interests., (© 2022 The Authors.)

Machine learning (ML) models to predict the toxicity of small molecules have garnered great attention and have become widely used in recent years. Computational toxicity prediction is particularly advantageous in the early stages of drug discovery in order to filter out molecules with high probability of failing in clinical trials. This has been helped by the increase in the number of large toxicology databases available. However, being an area of recent application, a greater understanding of the scope and applicability of ML methods is still necessary. There are various kinds of toxic end points that have been predicted in silico . Acute oral toxicity, hepatotoxicity, cardiotoxicity, mutagenicity, and the 12 Tox21 data end points are among the most commonly investigated. Machine learning methods exhibit different performances on different data sets due to dissimilar complexity, class distributions, or chemical space covered, which makes it hard to compare the performance of algorithms over different toxic end points. The general pipeline to predict toxicity using ML has already been analyzed in various reviews. In this contribution, we focus on the recent progress in the area and the outstanding challenges, making a detailed description of the state-of-the-art models implemented for each toxic end point. The type of molecular representation, the algorithm, and the evaluation metric used in each research work are explained and analyzed. A detailed description of end points that are usually predicted, their clinical relevance, the available databases, and the challenges they bring to the field are also highlighted., Competing Interests: The authors declare no competing financial interest., (© 2022 The Authors. Published by American Chemical Society.)

The use of high-throughput docking (HTD) in the drug discovery pipeline is today widely established. In spite of methodological improvements in docking accuracy (pose prediction), scoring power, ranking power, and screening power in HTD remain challenging. In fact, pose prediction is of critical importance in view of the pose-dependent scoring process, since incorrect poses will necessarily decrease the ranking power of scoring functions. The combination of results from different docking programs (consensus scoring) has been shown to improve the performance of HTD. Moreover, it has been also shown that a pose consensus approach might also result in database enrichment. We present a new methodology named Pose/Ranking Consensus (PRC) that combines both pose and ranking consensus approaches, to overcome the limitations of each stand-alone strategy. This approach has been developed using four docking programs (ICM, rDock, Auto Dock 4, and PLANTS; the first one is commercial, the other three are free). We undertook a thorough analysis for the best way of combining pose and rank strategies, and applied the PRC to a wide range of 34 targets sampling different protein families and binding site properties. Our approach exhibits an improved systematic performance in terms of enrichment factor and hit rate with respect to either pose consensus or consensus ranking alone strategies at a lower computational cost, while always ensuring the recovery of a suitable number of ligands. An analysis using four free docking programs (replacing ICM by Auto Dock Vina) displayed comparable results., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

We conducted a population-based incidence study in five New England states to quantify the risk of ulcerative keratitis associated with contact lens use among aphakic persons. All practicing ophthalmologists in the five-state area were surveyed to identify prospectively all new cases of ulcerative keratitis during a 4-month period. The number of aphakic persons using specific types of contact lenses was estimated through a telephone survey of 4178 households identified by random digit dialing. The annualized incidence of ulcerative keratitis among aphakic persons using contact lenses was estimated to be 52 cases per 10,000 aphakic contact lens wearers (95% confidence interval (CI), 31.1 to 86.9). The risk of ulcerative keratitis varied substantially by lens use, with extended wear having an estimated sevenfold greater risk relative to daily wear (95% CI, 1.6 to 30.2). Rates of ulcerative keratitis in aphakic persons using contact lenses were much greater than rates among cosmetic wearers of the same lens type: for daily-wear lenses, aphakic persons were estimated to have 6.3 times the risk of cosmetic wearers (95% CI, 1.9 to 21.0), and for extended-wear lenses, aphakic persons were estimated to have 8.7 times the risk of cosmetic wearers (95% CI, 3.5 to 21.9). These risks are useful in assessing the benefits and risks of contact lens wear as an alternative to other methods of aphakic correction.

The wearing of contact lenses has increased dramatically in the past decade; over 4 million people in the United States now use extended-wear soft contact lenses, and 9 million use daily-wear soft contact lenses. Numerous reports have caused concern that the use of soft contact lenses, especially extended-wear lenses, may result in a substantial risk of ulcerative keratitis. To examine this issue, we conducted a prospective study in five New England states to estimate the incidence of ulcerative keratitis among those who use cosmetic extended-wear and daily-wear soft contact lenses. To obtain the numerator for each estimate of incidence, we surveyed all practicing ophthalmologists in the study area to identify all new cases diagnosed over a four-month period. To provide the denominator, we conducted a survey of 4178 households to estimate the number of persons who wore each type of soft contact lens. The annualized incidence of ulcerative keratitis was estimated to be 20.9 per 10,000 persons using extended-wear soft contact lenses for cosmetic purposes and 4.1 per 10,000 persons using daily-wear soft contact lenses for cosmetic purposes (P less than 0.00001).