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Reaction step t as \({T}_{t}\). The implementation of each step in constructing the synthesis tree can be divided into four steps: First, reaction action (\({R}_{act}\)) sampling, with possible action types including "add", "extend”, "merge", and "end"; second, sampling of the first reactant (\({R}_{rct-1}\)); then, reaction template sampling (\({R}_{rxn}\)); finally, sampling of the second reactant (\({R}_{rct-2}\)). When \({R}_{act}\) = "Add", one or two new reactant nodes will be added to \({T}_{t}\), and a new product node will be generated under the given reaction template. When \({R}_{act}\) = "Expand", the most recently added node is treated as the first reactant, and a new product node will be added to \({T}_{t}\) if the given reaction template is for a unimolecular reaction; for a bimolecular reaction template, the second reactant is selected first, then the reaction is carried out according to the reaction template, adding a new reactant node and a product node to \({T}_{t}\). In the Syn-MolOpt frame only unimolecular and bimolecular reactions are allowed. When \({R}_{act}\) = "Merge", two root nodes act as reactants in a bimolecular reaction to produce a product molecule according to the given reaction template, adding a new product node to \({T}_{t}\) and merging two synthesis subtrees. When \({R}_{act}\) = "End", it indicates that the construction of the synthesis tree is complete. Based on the reaction templates, and the pre-processed set of purchasable building blocks, we first generated valid synthesis trees according to the construction method of the synthesis tree described above, then characterized the generated synthesis trees, and trained four neural networks as described before (\({N}_{act}\), \({N}_{rct-1}\), \({N}_{rxn}\), and \({N}_{rct-2}\)).As shown in Fig. 2B, molecular optimization is a conditional synthesis tree generation process, comprising five modules, using the concatenated embeddings of the target molecule to be optimized and the most recently added molecule in the synthesis tree as the current state embedding \({E}_{State-T}\) of the synthesis tree. Initially, \({E}_{State-T}\) serves as the input for both \({N}_{act}\) and \({N}_{rct-1}\), predicting the action type for the reaction step and the embedding of the first reactant \({E}_{rct-1}\), respectively. The embedding of the first reactant serves as a query to select the appropriate first reactant from the pre-processed set of building blocks using k-NN search. Next, the concatenated embedding of \({E}_{State-T}\) and \({E}_{rct-1}\) is input into \({N}_{rxn}\), which outputs a probability distribution of available reaction templates, and masks inapplicable reaction templates based on the first reactant, thus selecting a suitable reaction

Are further screened on the product-side using the same positively attributed substructures, but excluding templates with toxic groups on the product-side. This yields templates that successfully transform mutagenic substructures on the reactant-side. Thirdly, negatively attributed substructures (detoxifying groups) are used to filter the product-side of the reaction templates obtained in the second stage, selecting those with detoxifying groups. The resulting templates contain toxic groups on the reactant-side and detoxifying groups on the product-side, enabling the conversion of toxic groups. It is worth noting that functional templates often emerge from the first two steps, and the need of the third step depends on the availability of suitable templates. Since there might be overlap or encompassing of templates, manual intervention is required to ensure the independence and practicality of each template. Detailed information about the manual intervention can be found in the Supplementary Information.Fig. 1Overview of the functional reaction template library design process. A Functional substructure analysis: using SME to analyze the impacts of substructures on specific property; B Reaction template extraction: extracting reaction templates from open-source reaction datasets using RDChiral; and C Reaction template screening and management: screening reaction templates by substructure matchingFull size imageImplementation of Syn-MolOpt As illustrated in Fig. 2, the implementation of Syn-MolOpt consists of two stages: model training and molecular optimization. Syn-MolOpt models the synthesis pathway of a compound as a bottom-up synthesis tree (Fig. 2A), which each step modeled as a Markov decision process. This is achieved through training four neural networks (Fig. 2A) for the reaction action (\({R}_{act}\)), the first reactant (\({R}_{rct-1}\)), the reaction template (\({R}_{rxn}\)), and the second reactant (\({R}_{rct-2}\))\(.\) The performance of these four neural networks are detailed in Table S5 in the Supplementary Information. Subsequently, in the molecular optimization phase (Fig. 2B), these networks are used to predict \({R}_{act}\), \({R}_{rct-1}\), \({R}_{rxn}\), and \({R}_{rct-2}\). The predicted reactants, \({R}_{rct-1}\) and \({R}_{rct-2}\) (or \({R}_{rct-1}\) in the case of a unimolecular reaction template), undergo a reaction according to the predicted \({R}_{rxn}\). If the resulting mid-product matches a functional reaction template, functional processing is carried out. The synthesis tree is updated after each reaction step. Beyond utilizing functional reaction templates for structural modifications, a genetic algorithm (GA) is also used to numerically optimize the embeddings of the root molecules of the synthesis tree. The synthesis tree generator then decodes these optimized vectors to produce synthesizable molecules. This iterative process continues until molecules with the desired properties are

An update was planned, but for unknown reasons it appears that they will never release an update for transport rides.Junior Rides[]Helter SkelterHayrideFunslideMerry Go RoundBanana TycoonCanyon RunWestern WheelDowntown TrafficBugmaniaThe MontgolfiéresFamily Rides[]SpacewarsRings of SaturnSilly SwingsBucking BullWild Tycoon WingsCircusObservation TowerHaunted HouseFerris WheelTwisterTheatreTea CupsThrill Rides[]Super FlyerFlying CarpetPlundering PirateTwirling TowerBarnyard BashRing of FireSpinnerGravity FluxSlingshot TycoonOrbit SpinTornadoVertical SliceZero-G ZipperDrop ZoneTwisted TwizzlerZipperTyphoonPendulumRoller Coasters[]Looping CoasterHyper CoasterWooden CoasterStand-Up CoasterDive CoasterFloorless CoasterFlying CoasterInverted CoasterAccelerator CoasterWing CoasterSpinning CoasterWater Rides[]Water Rides are not available in this game. An update was planned, but for unknown reasons it appears that they will never release an update for water rides.Shops & Stalls[]Food & DrinkDrinksBurgersSushiSaladsItalianTacosSweets (Cotton Candy)SouvenirsBalloonsTeddy BearsFoam FingersSunglassesHatsT-ShirtsPark ServicesInfo BoothsBathroomsStaffMedicsJanitorsMechanicsEntertainersGallery[]A Forest Park - RCTW before the improvementsAn Adventure/Tropical Island Park - RCTW before the improvementsA Canyon Park - RCTW before the improvementsA Space Park - RCTW before the improvementsRCTW before the improvementsRCTW before the improvementsRCTW before the improvementsRCTW before the improvementsPark demo from PAX PrimeExternal links[]Official websiteRollerCoaster Tycoon 4 information at IGNRollerCoaster Tycoon 4 False Trailer Controversy @ Gamesided.com (2013)References[]↑ Tycoon GamesMain gamesRollerCoaster Tycoon: Added Attractions/Corkscrew Follies • Loopy LandscapesRollerCoaster Tycoon 2: Wacky Worlds • Time TwisterRollerCoaster Tycoon 3: Soaked! • Wild!RollerCoaster Tycoon WorldConsole gamesRollerCoaster Tycoon 3DRollerCoaster Tycoon JoyrideRollerCoaster Tycoon AdventuresMobile gamesRollerCoaster Tycoon 4 MobileRollerCoaster Tycoon TouchRollerCoaster Tycoon StoryCompilationsRCT Deluxe • RCT2 Triple Thrill Pack • RCT3 Gold! • RCT3 Gold Edition • RCT3 Platinum! • World of RCT • RCT 6 Pack • RCT Mega Pack • RCT3 Complete EditionPorts/remakesOpenRCT2 • RCT ClassicSpin-offsRCT: The Board Game • RCT Pinball Machine • RCT Idle

Template. If the chosen reaction template is for a bimolecular reaction, the concatenated embedding of \({E}_{State-T}\), \({E}_{rct-1}\), and the embedding of the predicted reaction template \({E}_{rxn}\) serve as the input for \({N}_{rct-2}\), outputting the embedding of the second reactant \({E}_{rct-2}\), which is then selected through the k-NN algorithm. Finally, the predicted first and second reactants (or the first reactant in the case of a unimolecular reaction template) react according to the predicted reaction template, and the resulting intermediate is matched with functional reaction templates, if a match is successful, functional processing is carried out. The synthesis tree is updated after each reaction step. Apart from molecular optimization through functional reaction templates during the construction of the synthesis tree, on the other hand, GA is used to perform numerical optimization on the embeddings of the root molecules of the synthesis tree. The GA operates on Morgan fingerprints with a configuration of 4096 bits and a radius of 2. The mutation is defined as flipping 24 bits in the fingerprint, occurring with a probability of 0.5. The population size is initially set to 128, and the offspring size generated in each iteration is 512. The algorithm runs for a maximum of 50 generations, with an early stop criterion activated if the increase in the population’s mean value is less than 0.01 across 10 consecutive generations, signaling convergence. The synthesis tree generator is then used as a decoder to obtain synthesizable molecules corresponding to the optimized vectors, and this process is repeated until the conditions are satisfied.Model construction and evaluationIn the construction of drug-likeness models for many real-world scenarios, a common practice is to integrate the prediction results of multiple models to obtain a consensus model. In our study, Mutag score, hERG score, CYP3A4 score and CYP2C19 score were the prediction results of their respective consensus models. When training the consensus model, each dataset is randomly divided into training, validation and test sets in a ratio of 8:1:1. First, 10 RGCN models based on different random seeds are constructed. Subsequently, a consensus model was constructed by integrating these 10 RGCN sub-models. The average of the predictions from the 10 submodels will be used as the output of the final consensus model. The four consensus models are all used for classification tasks, evaluated by the area under the receiver operating characteristic curve (ROC-AUC). When training the \({R}_{act}\) network, the \({R}_{rct-1}\) network, the \({R}_{rxn}\) network,

In our research, as they have not been continuously available over the 20-year period, and their respective impact factors are not in the same range as the “princeps” journals.Data collectionFirst, two authors (JP and ADJ) independently screened the studies retrieved by title and then by abstract for exclusion. They assessed the full text of possibly relevant studies for inclusion and exclusion criteria. Disagreement was resolved by discussion and arbitrated, if necessary, by a third author (SJ). Data were then added to an excel database, specifically designed for this review [15]. Journal of publication, year of publication, sample size, numbers of centers involved, country of the first author, number of countries participating, primary endpoint of the RCT, the result of the RCT according to its primary endpoint, the type of intervention tested, and the topic of the RCT were extracted.Journal of publication was classified either as a high-impact general journal (NEJM, Lancet, JAMA) or as a high-impact critical care journal (ICM, AJRCCM, CCM).The result of the RCT was classified either as unsignificant, significant for benefit, or significant for harm. We adapted a previously published classification [17] to include equivalence and non-inferiority designs. It was considered unsignificant if the P value was higher than 0.05 for superiority trial, or failed to prove the equivalence or the non-inferiority for equivalence and non-inferiority trials. The result of the RCT was considered significant for benefit if the P value was equal or lesser than 0.05 with a better outcome in the intervention group for superiority trials, or if the equivalence or non-inferiority was reached in equivalence or non-inferiority trials, or if the superiority of the intervention was reached in equivalence or non-inferiority trials. The result of the RCT was considered significant for harm if the P value was equal or lesser than 0.05 with a

RCTDeluxe fix for SteamDeck Mar 14 2023 Graphics Tool a quick and dirty mod to make the game display correctly fullscreen on the steam deck RCT Loopy Landscapes v1.20.013 Korean Patch Sep 17 2016 Patch This is the latest official patch for the Korean version of RollerCoaster Tycoon: Loopy Landscapes. Do not use it if you have Added Attractions or just... RCT Loopy Landscapes v1.20.013 Dutch Patch Sep 17 2016 Patch This is the latest official patch for the Dutch version of RollerCoaster Tycoon: Loopy Landscapes. Do not use it if you have Added Attractions or just... RCT Loopy Landscapes v1.20.013 Polish Patch Sep 17 2016 Patch This is the latest official patch for the Polish version of RollerCoaster Tycoon: Loopy Landscapes. Do not use it if you have Added Attractions or just... RCT Loopy Landscapes v1.20.013 German Patch Sep 17 2016 Patch This is the latest official patch for the German version of RollerCoaster Tycoon: Loopy Landscapes. Do not use it if you have Added Attractions or just... RCT Loopy Landscapes v1.20.013 French Patch Sep 17 2016 Patch This is the latest official patch for the French version of RollerCoaster Tycoon: Loopy Landscapes. Do not use it if you have Added Attractions or just... RollerCoaster Tycoon Woodies Tracks Sep 16 2016 Other This is a collection of wooden roller coasters, all designed for maximum excitement within a small area to ease construction. Ranging from the small and...