dataesr/scientific-paragraphs-categorization huggingface dataset:
The IntAct database contains approximately 900 interactions involving more than 200 microRNAs. Currently modest coverage, the data set is expected to increase, as new interactions are continually being added. The target mRNAs were annotated with the Ensembl transcript ID as identifier. In order to expedite the integration with other data identified by gene identifiers, the entries are also linked to the Ensembl gene ID. Referring to a specific transcript sequence allows the user to unequivocally annotate the interacting regions, confirmed by the mutagenesis analysis. Recent advances in RNA molecules stabilisation and delivery methods have renewed interest in RNA-based therapies. Chemical modifications of the nucleotides increase their resistance to nucleases and encapsulation of the microRNA mimics in neutral lipids allows the delivery of the molecules into living cells or organisms  [33].
In the last years several microRNA-based therapeutics have been developed, and some have entered phase II or III of clinical trials  [34]. The availability of exhaustive information about microRNA binding could accelerate the design of specific mimics or inhibitors, and to predict the effect of the perturbation on the neighbourhood interactors. Since RNA-RNA interactions occur through base pairing, the design and development of interfering molecules is easier than protein inhibitors, as demonstrated by the common use of complementary molecules in scientific papers. We have prioritised the annotation of microRNAs regulating genes involved in rare diseases, hopefully contributing to fill the gap of knowledge in this field. It is worth mentioning that the IntAct database has already collected a dataset of protein-protein interactions directly involved in rare-diseases  [35].
Some diseases are directly associated with genetic mutations or variants: the disease is a direct consequence of the mutation, or, more frequently, the mutation increases the probability of the onset or progression. When the mutation directly affects a PPI interaction, the information can be retrieved from the database  [36]. Very few papers describe the role of microRNA in rare diseases, and hopefully the list of interactions that affect genes associated to the diseases may help in elucidating their role. Although "rare" the "rare diseases" affect more than 30 million people in Europe, and are considered one of the major public health issues. As the return of investment in research on each individual disease may be limited, no treatment and diagnostic tests have been developed for many of them.
The annotation of genes involved in rare diseases has been recognised as a crucial step to expedite the diagnosis and to establish the appropriate treatment  [32]. We have expanded this knowledge by collecting existing data on the regulation of genes by microRNAs. The selected interactions are evaluated by IntAct curators and annotated according to approved CV terms. When more evidence supports an edge, a score of reliability is generated which helps the user to interpret the network  [37]. The collection of "true" interactors will simplify procedures to assess the value and potential unrelated effects of a therapeutic product, and will help the design of competitors.
Such an analysis requires that the engineering method employed will determine the (quantitative) contexts in which the correctness and safety of systems will be (qualitatively) evaluated. Integrating system design and operation sets more specific considerations for LEAS and the engineering approach: their safety will have to be assessed with respect to an ever evolving set of (critical) scenarios, instantiating difficult conditions for their sensors and underlying algorithms. In the FOCETA project, we provide a holistic methodology for designing and addressing the correctness and safety of LEAS, bridging the gap between the currently applied development, verification, and validation techniques for LE systems and their operation in the real world. We allow updates to the LECs, in response to emerging requirements from new scenarios, imperfect knowledge of the machine learning models (noise in data observations) or contextual misbehavior of them and possible security threats.
The role of specification languages, fairly well understood in the context of classical system design, is more ambiguous when reasoning about LEAS. The addition of LECs considerably adds to the system's complexity and requires rethinking the entire development and operation process, including the role of formal specifications, which we explain in this document. The FOCETA methodology (Fig. 1 ) includes two design flows. The first for designing trustworthy LECs, and the second for integrating them with classically engineered components, resulting in correct and safe LEAS, through iterative cycles of development and system operation. Our methodology relies on verifying simulation results and collecting additional data for system improvement. Runtime monitoring is used to supervise LEAS decision-making and record environmental situations.
Trustworthy Perception and Sensing An object detection component based on a neural network (NN) does not admit a natural formal specification of its expected behavior and characterizing a correct perception module's behavior using mathematics and logic is notoriously hard. It is even hard to formulate an appropriate verification and validation question for such LECs. There are, however, a number of more indirect but still effective approaches to reason about correctness of such components that we explore in FOCETA. Figure  2  provides a general framework illustrating how a machine learning based perception component might be developed and certified. It refines the usual development cycle, which forms three stages -training, offline V&V (or testing), and deployment -by including a collection of activities that support the certification of the component.
Two methods are considered. First, we developed runtime monitoring techniques for LECs. In general, these techniques can be divided into white-box and black-box approaches. The former class requires access to NN parameters. For example, neuron activation patterns or interval values can be recorded over the training dataset and applied as an abstraction for runtime monitoringe  [9, 18, 41]. Another approach of online monitoring developed in FOCETA uses small efficient neural networks to detect either hardware faults or input abnormalities efficiently as presented and shown in  [14]. The black-box approaches build verdicts based on the NN inputs and outputs. For example, temporal consistency of the detected objects can be modelled as a post-algorithmic abstraction and can be applied at runtime  [7].
Trustworthy Updates of LECs For a number of reasons, LECs usually have to be updated within the context of continuous engineering. For example, security concerns such as the need to withstand data perturbations (e. g. adversarial examples) or out-of-distribution data cases, trigger the need to replace LECs with improved ones that exhibit functionally comparable behavior in certain important aspects with the ones to be replaced. In FOCETA, in addition to the enhancement approaches discussed in Sect. 2. 1, this problem is addressed  [12]  by formally verifying the equivalence of two LECs as follows: for two pretrained NNs of different architectures, or of the same architecture with different parameters, we check whether they yield similar outputs for the same inputs. Contrary to strict functional equivalence that is desired for software component design, similar outputs do not necessarily mean identical outputs in the case of NNs.
The requirements must be transformed into formal specifications of monitorable properties (formalisation) and then mapped to a component-based simulation model of the LEAS. This transformation is based on predefined mappings of boilerplates to logic-based property specifications and it is supported by our requirements specification and formalisation tool. Formal Specification of Requirements. Adapting an existing specification formalism to LEAS is a challenging task because of the presence of LECs, such as CNN-based object detection modules, which do not admit natural logic-based formulation of their safety requirements (see Sect. 2). In FOCETA, we suggested past first-order linear temporal logic (P-FO-LTL) [5] as the main specification formalism to reason about LEAS. It abstracts away the internal structure of the NN-based LEC, including the internal values of the different neurons.
This simple, yet expressive formalism allows the user to (1) quantify over (possibly unbounded number of) agents, (2) define timing constraints between events and (3) naturally synthesize online monitors. The runtime verification of P-FO-LTL is implemented in the DejaVu tool  [17]  used in the project. We also use Signal Temporal Logic (STL)  [30]  as a popular specification language among the cyber-physical systems community. The main advantage of STL is that it naturally admits quantitative semantics and hence allows one to measure how far is the behavior from satisfying or violating a specification. A disadvantage of STL compared to P-FO-LTL is that it does not allow quantification over agents. The quantitative runtime STL monitors are implemented in the RTAMT library  [32]. Still it is evident that sequential specifications based on temporal logic cannot capture every aspect during design of LEAS.
In essence, the engineer can use diverse tools/models at different abstraction levels for modeling the sensing, control -actuation functions, the system's physical dynamics, and all interactions with its environment, as long as these tools/models can be integrated via standards-based interfaces (FMI  [40]  or TLM). V&V at design time is based on virtual testing. This encompasses all cases in which one or more physical elements (software, hardware) of the LEAS are replaced by their simulation model(s). The FOCETA Compositional Simulation Architecture supports model-in-the-loop (MiL), software-in-the-loop (SiL) and hardware-in-the-loop (HiL) testing of the systems functions, at various abstraction levels. MiL testing is focused on design correctness, the performance of LECs, the control strategies and the associated trade-offs.
Having done MiL testing, fine-tuning processes using different mechanisms can be applied, such as requirement-encoded and safety-oriented loss function  [27]  for object detection or uncertainty-aware loss function  [25]  for trajectory prediction tasks. SiL testing is used to check the correctness of code in closed loop with a model of the physical system. Finally, HiL testing consists of real-time simulations that include the target hardware; the hardware-specific impact can be also simulated. A productionfriendly approach has been developed with the PyTorchALFI tool chain  [29] , which allows to efficiently perform fault injection  [16]. For verifying safety properties for the overall system model, the online formal analysis of simulation traces is supported. Within the context of the FOCETA Compositional Simulation Architecture, this takes place through the integration of runtime monitors, which are generated by the DejaVU and RTAMT monitor synthesis tools based on formal specifications in P-FO-LTL  [38]  and Signal Temporal Logic (STL), respectively.
In this case study, a formal model was developed for the perception and throttle/brake control modules of the LEAS using the BIP component framework. This executable model was then integrated with a Simcenter Prescan model for the environment, a Simcenter Amesim model for the EGO vehicle dynamics and a ROS-based implementation of the steering control for path lane following and twist control. The vehicle has an RGB camera sensor. The camera feed is sent to the BIP model for speed sign detection and classification, and the controller component takes the action of acceleration/deceleration based on the speed sign detected. Formal specifications derived from the system requirements using the FOCETA requirements formalisation tool (cf. Figure  4 ) are then used for synthesizing DeJaVu runtime monitors for safety property checking.
One example of such an iterative approach is the tool HASTEN (macHine leArning booSTEd dockiNg). 9  HASTEN starts from an initial random compound selection from a large chemical library for a first conventional docking run and trains a ML model with the obtained data. Then, scores for the full compound library are predicted and used to rank all compounds. The best-ranked compounds next get selected for docking in the following iteration. This deliberate bias towards top-scoring compounds has been shown to result in excellent recall for datasets on the millionscale. 9  We aimed to investigate the applicability and performance of the HASTEN approach on even larger datasets, where the training data already reaches the million-scale and predictions on the giga-scale cover billions of compounds.
To evaluate how the ML-boosted approach performs on giga-scale chemical libraries, such libraries first need to be screened using standard brute-force docking to obtain the baseline data. Such docking data on the giga-scale has so far at least rarely been made publicly available: To date, the only example we are aware of are the Covid-19 screening results obtained with AutoDock-GPU on the Oak Ridge National Laboratory Summit-computer. 12, 13  Herein, we used the fast Glide HTVS method in one of the largest conventional docking campaigns performed to date, to provide a brute-force docking baseline for analysis and comparison with our ML-boosted approach. 14  We selected two distinct targets based on ongoing academic drug discovery projects: The first target, the SurA protein, is a periplasmic chaperone found in Gram-negative bacteria.
We further demonstrate how large numbers of compounds which fail to dock successfully can hamper the HASTEN approach, and discuss different options to handle such 'failed' compounds. Finally, to support future screening approaches, we release a prepared version of the Enamine REAL lead-like screening library used in this study in a Glide-compatible format. Additionally, we release our full giga-scale docking results as benchmarking datasets for the future development and improvement of ML-boosted screening procedures.
1 GHz, 384 GB of system memory, and a local 3. 6 TB NVMe disk. Screening database preparation. The Enamine REAL lead-like (ERLL) library, containing a total of 1. 56 billion compounds (March 2021), was selected for the giga-scale screening. 5  All included compounds have lead-like properties, with molecular weight ≤460 Da, SlogP -4. 0 to 4. 2, number of hydrogen bond acceptors <10 and donors <5, number of ring systems ≤4 and rotatable bonds ≤10. The library was obtained in ChemAxon extended SMILES format and converted to regular SMILES using RDKit v2021. 03. 5, retaining the stereochemical information of the compounds where applicable. 27, 28  To reduce the variation in docking times between different subsets, compound order first was randomized, and SMILES were then evenly divided into 20 subsets.
Ligand 3D structure preparation for docking was carried out with Schrödinger LigPrep (Schrödinger Suite 2021-1). 29  Up to 8 tautomers per compound and 4 stereoisomers per tautomer for a target pH of 7. 0 ± 1. 5 were generated and compound geometries were energy-minimized with the OPLS_2005 forcefield. Pre-prepared compounds were next collected into Schrödinger Phase databases for use during docking and future studies. [29] [30] [31]  Coordinates were stored in the compact internal coordinate representation, and a single conformation was generated with rapid sampling from each input compound during the Phase revise step. To enable parallel processing within the CSC system wall time limits, the 20 input files were further split into a total of 781 individual Phase databases of approx. 4. 8 million compounds each.
Receptor preparation and SiteMap analysis. Structures of SurA (PDB-ID 1m5y, chain A)  32  and GAK (PDB-ID 4y8d, chain A)  33  were retrieved from the RCSB Protein Data Bank. 34  The Schrödinger Protein Preparation Wizard was used for structure preparation, hydrogen addition, and bond order assignment. Missing sidechains were added with Prime. 29  Crystallographic agents and water molecules were deleted. State generation for the original crystallographic ligand in the GAK structure was performed with Epik for pH 7. 0±2. 0. Amino acid protonation states for pH 7. 0 were assigned with PROPKA and the hydrogen bonding network was optimized. Receptors were then subjected to a restrained energy minimization in the OPLS_2005 forcefield until the heavy atom RMSD compared to the previous minimization step fell below 0.
15 e and nonplanar amide conformations were penalized in both targets. Additionally, for GAK, the hydrogen-bonding constraint on the hinge-region amide of Cys126 was used. With HTVS mode, the OPLS_2005 forcefield was used, and a single pose per ligand was collected after subjecting 5 poses to postdocking minimization. Simulated ML-boosted docking with HASTEN. The machine learning-boosted docking was simulated using the simu-dock mode in a local implementation of HASTEN v0. 2 (optimized for CSC Puhti). 9  For ML, Chemprop v1. 3. 1 (with Python 3. 8. 12) inside a singularity-container was used. 38  Briefly, simu-dock allows the use of pre-generated docking data instead of actual docking in the HASTEN procedure. Whenever a compound is selected for docking by the algorithm, the pre-generated results of the conventional docking study will be loaded.
Since scores are only added to the training data when compounds were selected for docking, the system exhibits identical behavior to screening with HASTEN when run including the brute-force docking steps. In the first iteration, training was initialized with a random selection of 0. 1% of the full library (1. 56 million compounds). The selected compound subset was split randomly into training, validation, and test sets amounting to 80%, 10%, and 10% of the selected compounds, respectively. When no docking score was obtained during the conventional docking run (i. e. the compound did not dock successfully or failed to satisfy the constraint in GAK), an arbitrary failed score of +5. 0 or 0. 0 was applied, or failed compounds were excluded entirely from the training data. We performed one round of HASTEN with each treatment of failed compounds for each target.
In conclusion, the two targets chosen for our case study display diverging binding pocket properties and can consequently be expected to favorably interact with different chemical scaffolds, thereby allowing us to investigate the method's performance on the giga-scale in two distinct screening scenarios. A ready-to-use Glide-compatible screening library and giga-scale brute-force docking to SurA and GAK. The first step towards the generation of a brute-force docking baseline was the preparation of ligand 3D structures from the original 1. 56 billion input SMILES. Relevant tautomers and stereoisomers increased the library size to approx. 3. 8 billion structures, which we distributed in 781 ligand databases for parallel docking. Preparation of this screening library took around 30 days (457 600 CPU hours) when utilizing 640 CPUs on the CSC Mahti supercomputer.
Next, we performed the brute-force docking of the complete prepared 3. 8 billion structures to the SurA and GAK targets using Schrödinger Glide in HTVS mode to generate the docking baseline for later comparison. 14  With an approximate processing capacity of 40 compounds per minute per CPU core, Glide HTVS proved to be the fastest available method to generate a gigascale docking dataset. Using 640 CPUs of the CSC Mahti supercomputer, we spent 85 days (1 305   216 CPU hours) on SurA docking, and the GAK conventional screening was completed in 53 days (809 216 CPU hours). In our setting, the library preparation step contributed approx. 30% to the total required time for the full brute-force docking study. To support future screening efforts and enable a time reduction during the ligand preparation step, we release the entire prepared and randomized Enamine REAL lead-like library (March 2021) in 781 Glide-compatible, ready-to-use Phase databases (https: //doi.
org/10. 23729/2de314bb-59af-452a-955c-c2ff0c5ea57f). Moreover, we acknowledge that brute-force approaches to screening efforts on this scale remain elusive in most settings, even when using the fastest available docking methods. At the same time, giga-scale libraries are becoming more and more common and novel approaches to, e. g. , ML-boosted docking, should thus be evaluated on giga-scale datasets. In the hope of providing a useful benchmarking dataset for such future applications, we further release our full giga-scale docking results for the two targets SurA and GAK. (https://doi. org/10. 23729/2170dc9c-4905-43c3-aeee-a574d360737f) ML-boosted giga-scale screening of SurA and GAK. For accelerating the screening process with the help of ML, we used the tool HASTEN, which has been previously validated on million-scale datasets.
9  HASTEN aims to identify the top-scoring compounds rather than attempting a generalized prediction of docking scores for a given target. By iteratively selecting compounds with the best predicted scores, the training data will be progressively enriched in both true positives (already ranked correctly by the model) and false positives (ranked highly by the model, although the compounds dock poorly). This will improve HASTEN's capability of identifying true top-scoring compounds with every iteration. Figure  1  summarizes our adapted procedure for the giga-scale screening. We first started from an initial random selection of 0. 1% of the 1. 56 billion compounds. Previous work with HASTEN for million-scale data typically involved the selection of 1% of the library on each iteration, but since our training data at 0.
1% already exceeds one million compounds, we decided to instead aim for a final total docking of 1% of the giga-scale library. The brute-force docking step was simulated by utilizing the pre-generated docking data for the complete screening library: Docking scores of compounds that were selected for docking by the algorithm were loaded directly. Only scoring data of selected compounds was considered, which ensures HASTEN to run as if the brute-force docking step had been performed as part of the workflow. Docking scores and corresponding compound SMILES were used as input data for training a ML model with Chemprop. 38  Next, with the generated model, docking scores for the full ERLL library were predicted, and compounds were ranked by their predicted scores. During the next iteration, docking results for the top-ranked 0.
1% of the compounds were added to the training data. This process was repeated nine times to end with a total training dataset amounting to 1% of the full giga-scale dataset. Figure  1 : Overview of the HASTEN workflow for the ML-boosted giga-scale screening against SurA and GAK targets. In the conventional docking step (highlighted in orange), brute-force docking was simulated by loading the docking scores of selected compounds from the pre-generated brute-force docking dataset. The ML procedure (highlighted in blue) involves a training step with all docking data of compounds that were selected for docking until the current iteration, followed by full giga-scale library prediction. Reducing the required number of compounds to dock to 1% of the full library lowered the total time spent on ligand preparation and SurA docking to around one day and 4 hours when utilizing 640 CPUs of the CSC Mahti supercomputer, and 20 hours for the GAK target (including ligand preparation; 17628 and 12668 CPU hours for SurA and GAK, respectively).