A: A Quantitative Structure-Activity Relationship (QSAR) model is a predictive model that relates the structure of a chemical (encoded in a series of numerical descriptors) to one of its properties. QSAR models use pre-existing experimental data, analysed through machine-learning algorithms, to establish this relationship. These models are statistically validated with separate training and validation datasets to ensure predictability.

A: There is extensive literature on the technique. You can find more information on our website (https://protoqsar.com/publicaciones/modelos-qsar/). Additionally, you can find our courses on developing and applying QSAR and other computational techniques at https://chemopredictionacademy.com/.

A: Yes. Most regulatory frameworks not only accept QSAR models but also encourage their use as an alternative to animal-based tests.

A: No, except for ProtoNANO, our models are developed to work with organic discrete species. Thus, any chemical containing metals or other elements not common in organic molecules is not accepted. Additionally, mixtures and salts are not directly usable in ProtoPRED. In such cases, the user can predict the properties separately for individual ions or components.

A: Some of our QSAR models are available thorugh different modules because they can be applied in a regulatory application or combined with other results. The QSAR model is the same, and the results should be the same but some information included in the PDF reports may be sligthly different, as those are tailored for the particular application and/or include combined reports which consider additional information.

A: ProtoPRED is commercial software. However, at present, you can register for free and gain unlimited access for a limited time.

A: ProtoPRED tokens are the units used by the server to manage your credit. Each time a prediction is made or a PDF report is generated, a specific number of tokens is deducted from your account.

A: Even if you are accessing ProtoPRED for free, tokens are still used to monitor usage. Please feel free to contact us to request additional tokens.

A: To purchase tokens, simply visit our website at https://protopred.com/, select the desired token package, add it to your cart, and proceed with the purchase. You can pay using a credit card.

A: ProtoPRED stores information of the user in 4 different forms.

• Personal information provided during the registration. This information is stored by ProtoQSAR, but we do not use it for any other purposes than provide the service (unless the permission is explicitly give during the registration process).
• Historic of the movements. All the movements done in the server that affect your tokens (such as predictions, donwload of reports, credit purchases) can be consulted in the user page, to ensure proper record of the use of the server. By default, this historic includes data such as the SMILES and the predicted value, to facilitate user revision, but this can be changed in the user section.
• Last prediction. Complete results of the last prediction of each module is also stored internally, to allow the user to recover the data in case of accidental closure, connectivity issues or any other interruption. This can be also changed in the user section to ensure data privacy.
• QPRF data. To simplify the filling of the author data section in the QPRF (obligatory), default values are offered for the users name, address, phone and email. The user can edit those fields in the user page. For each PDF request, the current values can be stored as new defaults (this is optional).

A: In the user page, there is a Reports data section to modify the QPRF author data. This form includes a check box for Data Privacy. If this is selected, you will remove all the information about your predictions results (only the models used and time) and this will be not stored from that moment. Note that the data is really erased and cannot be recovered. This also will deactivate the option to recover the results of the last prediction for each module.

A: Yes. For queries about your data, you can contact ProtoQSAR data representatives at gdpr@protoqsar.com.

A: ProtoPRED can produce four different types of output: the results page always includes a summary table with all the predictions performed and an additional model-specific table with additional details for each different model, as well as an option to download an Excel spreadsheet (with more information such as the values in the internal model units). Additionally, you can download standardised PDF reports (this could have an additional cast) such as QPRFs of all the QSAR predictions (except for ProtoNANO) and specific combined reports for ProtoICH and GenoITS.

A: ProtoPRED uses calculated molecular descriptors to characterise your substance. In some cases, these descriptors cannot be calculated due to numerical issues or incompatibilities between the structure and the descriptors. When this occurs, ProtoPRED uses imputation to estimate the values from our database. However, this is only permitted for a limited number of descriptors. If an excessive number of values require imputation, the prediction will not be provided because it will not be considered a reliable predcition. In such cases, the results will be included in the Excel and HTML outputs, but a PDF report cannot be downloaded as no prediction is available.

A: QSAR models are trained using a database of molecules and experimental values, so their validity is restricted to the chemical space defined by this dataset. ProtoPRED uses four different techniques to evaluate this chemical space and to evaluate if your target molecule is within it. A prediction is always provided, but those outside the applicability domain are considered less reliable.

A: Assessing the reliability of a calculation implies factors such as the overall predictivity of the model, the relationship of the target molecule with the training set, the behavior of model with similar substances and the adequacy of the input. To help the user, ProtoPRED provides a combined reliability score using different parameters (list of parameters and individual values are in the QPRF). Note that this score is presented as a guidance and does not substitute a tailored expert assessment considering the details of each factor included in the QPRF.

A: No. The information on analogues is provided to assess the prediction. Analogues may or may not be part of the model's training set but do not contribute directly to the prediction. However, they do influence the reliability score of the prediction.

A: You can provide content for various sections of the report, such as those regarding your data, the identity of the substance, and additional comments in different sections. However, it is currently not possible to modify the existing content directly. ProtoPRED offers an automatic QPRF tool to assist users, but the QPRF is submitted by the user, allowing you to use the information to create your own version of the document.

A: Please, try again. If the problem persist, contact us at protopred@protosar.com

A: Yes. You can use the Get results from the last calculation button of each module (located above the INPUT MOLECULE(S) section). Note that this option should not work if the Data privacy options is selected in the reports data section of the user page

A: Your tokens should be added to the account name provided in the purchase form within 24 hours. You will receive a confirmation email once your account has been updated. Please log in again to verify the update. If the tokens do not appear, contact protopred@protoqsar.com with a copy of the purchase confirmation.

A: The ICH M7 guideline requires a dual computational approach for the assessment of mutagenicity: statistical and expert-rule methods. Additionally, certain classes of chemicals (known as carcinogenicity cohort of concern) are characterised by their high mutagenic potency and have specific regulatory considerations. Therefore, a profiler is included to detect these species.

A: ProtoPRED includes buttons to automatically select the endpoints required for each of the Annexes in REACH, based on the substance's tonnage. However, this serves as general guidance, as the exact requirements may vary depending on existing data for these parameters or other properties.