Machine Learning Toxicity Prediction: Latest Advances by Toxicity End Point

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.Fil: Cavasotto, Claudio Norberto. Universidad Austral. Facultad de Ciencias Biomédicas. Instituto de Investigaciones en Medicina Traslacional. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones en Medicina Traslacional; ArgentinaFil: Scardino, Valeria. Universidad Austral; Argentin

Relevance of mytilid shell microtopographies for fouling defence - a global comparison

Prevention of epibiosis is of vital importance for most aquatic organisms, which can have consequences for their ability to invade new areas. Surface microtopography of the shell periostracum has been shown to have antifouling properties for mytilid mussels, and the topography shows regional differences. This article examines whether an optimal shell design exists and evaluates the degree to which shell microstructure is matched with the properties of the local fouling community. Biomimics of four mytilid species from different regional provenances were exposed at eight different sites in both northern and southern hemispheres. Tendencies of the microtopography to both inhibit and facilitate fouling were detected after 3 and 6 weeks of immersion. However, on a global scale, all microtopographies failed to prevent fouling in a consistent manner when exposed to various fouling communities and when decoupled from other shell properties. It is therefore suggested that the recently discovered chemical anti-microfouling properties of the periostracum complement the anti-macrofouling defence offered by shell microtopography

Relative Sea-Level Rise and Potential Submersion Risk for 2100 on 16 Coastal Plains of the Mediterranean Sea.

The coasts of the Mediterranean Sea are dynamic habitats in which human activities have been conducted for centuries and which feature micro-tidal environments with about 0.40 m of range. For this reason, human settlements are still concentrated along a narrow coastline strip, where any change in the sea level and coastal dynamics may impact anthropic activities. In the frame of the RITMARE and the Copernicus Projects, we analyzed light detection and ranging (LiDAR) and Copernicus Earth Observation data to provide estimates of potential marine submersion for 2100 for 16 small-sized coastal plains located in the Italian peninsula and four Mediterranean countries (France, Spain, Tunisia, Cyprus) all characterized by different geological, tectonic and morphological features. The objective of this multidisciplinary study is to provide the first maps of sea-level rise scenarios for 2100 for the IPCC RCP 8.5 and Rahmstorf (2007) projections for the above affected coastal zones, which are the locations of touristic resorts, railways, airports and heritage sites. On the basis of our model (eustatic projection for 2100, glaciohydrostasy values and tectonic vertical movement), we provide 16 high-definition submersion maps. We estimated a potential loss of land for the above areas of between about 148 km(2)(IPCC-RCP8.5 scenario) and 192 km(2)(Rahmstorf scenario), along a coastline length of about 400 km

Combination of pose and rank consensus in docking-based virtual screening: the best of both worlds

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 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 targets sampling different protein families with a variety of 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

Combination of pose and rank consensus in docking-based virtual screening: The best of both worlds

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.Fil: Scardino, Valeria. Universidad Austral; ArgentinaFil: Bollini, Mariela. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Investigaciones en Bionanociencias "Elizabeth Jares Erijman"; ArgentinaFil: Cavasotto, Claudio Norberto. Universidad Austral. Facultad de Ciencias Biomédicas. Instituto de Investigaciones en Medicina Traslacional. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones en Medicina Traslacional; Argentin

How good are AlphaFold models for docking-based virtual screening?

Summary: 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