Machine Learning Scoring Functions for Drug Discoveries from Experimental and Computer-Generated Protein-Ligand Structures: Towards Per-Target Scoring Functions

In recent years, machine learning has been proposed as a promising strategy to build accurate scoring functions for computational docking finalized to numerically empowered drug discovery. However, the latest studies have suggested that over-optimistic results had been reported due to the correlations present in the experimental databases used for training and testing. Here, we investigate the performance of an artificial neural network in binding affinity predictions, comparing results obtained using both experimental protein-ligand structures as well as larger sets of computer-generated structures created using commercial software. Interestingly, similar performances are obtained on both databases. We find a noticeable performance suppression when moving from random horizontal tests to vertical tests performed on target proteins not included in the training data. The possibility to train the network on relatively easily created computer-generated databases leads us to explore per-target scoring functions, trained and tested ad-hoc on complexes including only one target protein. Encouraging results are obtained, depending on the type of protein being addressed.Comment: 22 pages, 8 figure

The Power of Non-Determinism in Higher-Order Implicit Complexity

We investigate the power of non-determinism in purely functional programming languages with higher-order types. Specifically, we consider cons-free programs of varying data orders, equipped with explicit non-deterministic choice. Cons-freeness roughly means that data constructors cannot occur in function bodies and all manipulation of storage space thus has to happen indirectly using the call stack. While cons-free programs have previously been used by several authors to characterise complexity classes, the work on non-deterministic programs has almost exclusively considered programs of data order 0. Previous work has shown that adding explicit non-determinism to cons-free programs taking data of order 0 does not increase expressivity; we prove that this - dramatically - is not the case for higher data orders: adding non-determinism to programs with data order at least 1 allows for a characterisation of the entire class of elementary-time decidable sets. Finally we show how, even with non-deterministic choice, the original hierarchy of characterisations is restored by imposing different restrictions.Comment: pre-edition version of a paper accepted for publication at ESOP'1

Neuro-inflammatory effects of photodegradative products of bilirubin

Phototherapy was introduced in the early 1950\u2019s, and is the primary treatment of severe neonatal jaundice or Crigler-Najjar syndrome. Nevertheless, the potential biological effects of the products generated from the photodegradation of bilirubin during phototherapy remain unknown. This is very relevant in light of recent clinical observations demonstrating that the use of aggressive phototherapy can increase morbidity or even mortality, in extremely low birthweight (ELBW) infants. The aim of our study was to investigate the effects of bilirubin, lumirubin (LR, its major photo-oxidative product), and BOX A and B (its monopyrrolic oxidative products) on the central nervous system (CNS) using in vitro and ex vivo experimental models. The effects of bilirubin photoproducts on cell viability and expression of selected genes were tested in human fibroblasts, three human CNS cell lines (neuroblastoma SH-SY5Y, microglial HMC3, and glioblastoma U-87 cell lines), and organotypic rat hippocampal slices. Neither bilirubin nor its photo-oxidative products affected cell viability in any of our models. In contrast, LR in biologically-relevant concentrations (25\u2009\u3bcM) significantly increased gene expression of several pro-inflammatory genes as well as production of TNF-\u3b1 in organotypic rat hippocampal slices. These findings might underlie the adverse outcomes observed in ELBW infants undergoing aggressive phototherapy

New 2,6,9-trisubstituted adenines as adenosine receptor antagonists: a preliminary SAR profile

A new series of 2,6,9-trisubstituted adenines (5–14) have been prepared and evaluated in radioligand binding studies for their affinity at the human A1, A2A and A3 adenosine receptors and in adenylyl cyclase experiments for their potency at the human A2B subtype. From this preliminary study the conclusion can be drawn that introduction of bulky chains at the N6 position of 9-propyladenine significantly increased binding affinity at the human A1 and A3 adenosine receptors, while the presence of a chlorine atom at the 2 position resulted in a not univocal effect, depending on the receptor subtype and/or on the substituent present in the N6 position. However, in all cases, the presence in the 2 position of a chlorine atom favoured the interaction with the A2A subtype. These results demonstrated that, although the synthesized compounds were found to be quite inactive at the human A2B subtype, adenine is a useful template for further development of simplified adenosine receptor antagonists with distinct receptor selectivity profiles

Camtrap DP: an open standard for the FAIR exchange and archiving of camera trap data

Camera trapping has revolutionized wildlife ecology and conservation by providing automated data acquisition, leading to the accumulation of massive amounts of camera trap data worldwide. Although management and processing of camera trap-derived Big Data are becoming increasingly solvable with the help of scalable cyber-infrastructures, harmonization and exchange of the data remain limited, hindering its full potential. There is currently no widely accepted standard for exchanging camera trap data. The only existing proposal, “Camera Trap Metadata Standard” (CTMS), has several technical shortcomings and limited adoption. We present a new data exchange format, the Camera Trap Data Package (Camtrap DP), designed to allow users to easily exchange, harmonize and archive camera trap data at local to global scales. Camtrap DP structures camera trap data in a simple yet flexible data model consisting of three tables (Deployments, Media and Observations) that supports a wide range of camera deployment designs, classification techniques (e.g., human and AI, media-based and event-based) and analytical use cases, from compiling species occurrence data through distribution, occupancy and activity modeling to density estimation. The format further achieves interoperability by building upon existing standards, Frictionless Data Package in particular, which is supported by a suite of open software tools to read and validate data. Camtrap DP is the consensus of a long, in-depth, consultation and outreach process with standard and software developers, the main existing camera trap data management platforms, major players in the field of camera trapping and the Global Biodiversity Information Facility (GBIF). Under the umbrella of the Biodiversity Information Standards (TDWG), Camtrap DP has been developed openly, collaboratively and with version control from the start. We encourage camera trapping users and developers to join the discussion and contribute to the further development and adoption of this standard. Biodiversity data, camera traps, data exchange, data sharing, information standardspublishedVersio