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Despite extensive research in the field of thrombotic diseases, the prevention of blood clots remains an important area of study. Therefore, the development of new anticoagulant drugs with better therapeutic profiles and fewer side effects to combat thrombus formation is still needed. Herein, we report the synthesis and evaluation of novel pyrroloquinolinedione-based rhodanine derivatives, which were chosen from 24 developed derivatives by docking as potential molecules to inhibit the clotting factors Xa and XIa. For the synthesis of new hybrid derivatives of pyrrolo[3,2,1-ij]quinoline-2-one, we used a convenient structural modification of the tetrahydroquinoline fragment by varying the substituents in positions 2, 4, and 6. In addition, the design of target molecules was achieved by alkylating the amino group of the rhodanine fragment with propargyl bromide or by replacing the rhodanine fragment with 2-thioxoimidazolidin-4-one. The in vitro testing showed that eight derivatives are capable of inhibiting both coagulation factors, two compounds are selective inhibitors of factor Xa, and two compounds are selective inhibitors of factor XIa. Overall, these data indicate the potential anticoagulant activity of these molecules through the inhibition of the coagulation factors Xa and XIa.

Despite extensive research in the field of thrombotic diseases, the prevention of blood clots remains an important area of study. Therefore, the development of new anticoagulant drugs with better therapeutic profiles and fewer side effects to combat thrombus formation is still needed. Herein, we report the synthesis and evaluation of novel pyrroloquinolinedione-based rhodanine derivatives, which were chosen from 24 developed derivatives by docking as potential molecules to inhibit the clotting factors Xa and XIa. For the synthesis of new hybrid derivatives of pyrrolo[3,2,1-ij]quinoline-2-one, we used a convenient structural modification of the tetrahydroquinoline fragment by varying the substituents in positions 2, 4, and 6. In addition, the design of target molecules was achieved by alkylating the amino group of the rhodanine fragment with propargyl bromide or by replacing the rhodanine fragment with 2-thioxoimidazolidin-4-one. The in vitro testing showed that eight derivatives are capable of inhibiting both coagulation factors, two compounds are selective inhibitors of factor Xa, and two compounds are selective inhibitors of factor XIa. Overall, these data indicate the potential anticoagulant activity of these molecules through the inhibition of the coagulation factors Xa and XIa.Despite extensive research in the field of thrombotic diseases, the prevention of blood clots remains an important area of study. Therefore, the development of new anticoagulant drugs with better therapeutic profiles and fewer side effects to combat thrombus formation is still needed. Herein, we report the synthesis and evaluation of novel pyrroloquinolinedione-based rhodanine derivatives, which were chosen from 24 developed derivatives by docking as potential molecules to inhibit the clotting factors Xa and XIa. For the synthesis of new hybrid derivatives of pyrrolo[3,2,1-ij]quinoline-2-one, we used a convenient structural modification of the tetrahydroquinoline fragment by varying the substituents in positions 2, 4, and 6. In addition, the design of target molecules was achieved by alkylating the amino group of the rhodanine fragment with propargyl bromide or by replacing the rhodanine fragment with 2-thioxoimidazolidin-4-one. The in vitro testing showed that eight derivatives are capable of inhibiting both coagulation factors, two compounds are selective inhibitors of factor Xa, and two compounds are selective inhibitors of factor XIa. Overall, these data indicate the potential anticoagulant activity of these molecules through the inhibition of the coagulation factors Xa and XIa.

Coagulation factor Xa and factor XIa are proven to be convenient and crucial protein targets for treatment for thrombotic disorders and thereby their inhibitors can serve as effective anticoagulant drugs. In the present work, we focused on the structure–activity relationships of derivatives of pyrrolo[3,2,1-ij]quinolin-2(1H)-one and an evaluation of their activity against factor Xa and factor XIa. For this, docking-guided synthesis of nine compounds based on pyrrolo[3,2,1-ij]quinolin-2(1H)-one was carried out. For the synthesis of new hybrid hydropyrrolo[3,2,1-ij]quinolin-2(1H)-one derivatives, we used convenient structural modification of both the tetrahydro- and dihydroquinoline moiety by varying the substituents at the C6,8,9 positions. In vitro testing revealed that four derivatives were able to inhibit both coagulation factors and three compounds were selective factor XIa inhibitors. An IC50 value of 3.68 μM for was found for the best factor Xa inhibitor and 2 μM for the best factor XIa inhibitor.

In the modern world, complications caused by disorders in the blood coagulation system are found in almost all areas of medicine. Thus, the development of new, more advanced drugs that can prevent pathological conditions without disrupting normal hemostasis is an urgent task. The blood coagulation factor XIIa is one of the most promising therapeutic targets for the development of anticoagulants based on its inhibitors. The initial stage of drug development is directly related to computational methods of searching for a lead compound. In this study, docking followed by quantum chemical calculations was used to search for noncovalent low-molecular-weight factor XIIa inhibitors in a focused library of druglike compounds. As a result of the study, four low-molecular-weight compounds were experimentally confirmed as factor XIIa inhibitors. Selectivity testing revealed that two of the identified factor XIIa inhibitors were selective over the coagulation factors Xa and XIa.

Coagulation factor Xa and factor XIa are proven to be convenient and crucial protein targets for treatment for thrombotic disorders and thereby their inhibitors can serve as effective anticoagulant drugs. In the present work, we focused on the structure-activity relationships of derivatives of pyrrolo[3,2,1-ij]quinolin-2(1H)-one and an evaluation of their activity against factor Xa and factor XIa. For this, docking-guided synthesis of nine compounds based on pyrrolo[3,2,1-ij]quinolin-2(1H)-one was carried out. For the synthesis of new hybrid hydropyrrolo[3,2,1-ij]quinolin-2(1H)-one derivatives, we used convenient structural modification of both the tetrahydro- and dihydroquinoline moiety by varying the substituents at the C6,8,9 positions. In vitro testing revealed that four derivatives were able to inhibit both coagulation factors and three compounds were selective factor XIa inhibitors. An IC50 value of 3.68 μM for was found for the best factor Xa inhibitor and 2 μM for the best factor XIa inhibitor.Coagulation factor Xa and factor XIa are proven to be convenient and crucial protein targets for treatment for thrombotic disorders and thereby their inhibitors can serve as effective anticoagulant drugs. In the present work, we focused on the structure-activity relationships of derivatives of pyrrolo[3,2,1-ij]quinolin-2(1H)-one and an evaluation of their activity against factor Xa and factor XIa. For this, docking-guided synthesis of nine compounds based on pyrrolo[3,2,1-ij]quinolin-2(1H)-one was carried out. For the synthesis of new hybrid hydropyrrolo[3,2,1-ij]quinolin-2(1H)-one derivatives, we used convenient structural modification of both the tetrahydro- and dihydroquinoline moiety by varying the substituents at the C6,8,9 positions. In vitro testing revealed that four derivatives were able to inhibit both coagulation factors and three compounds were selective factor XIa inhibitors. An IC50 value of 3.68 μM for was found for the best factor Xa inhibitor and 2 μM for the best factor XIa inhibitor.

Drug-target affinity (DTA) prediction is a fundamental problem in drug discovery. Computational methods for predicting DTA can greatly assist drug design by decreasing the search space and reducing the number of protein-ligand complexes with low affinity. Currently DTA approaches often do not require protein three-dimensional (3D) structural information, which is often not accessible. In this study we present the DrugForm-DTA model, which uses only structure-less representations of ligand and protein. It is a Transformer-based neural network with protein encoding based on ESM, and small molecule ligand encoding obtained with Chemformer. We evaluated the model on standard benchmarks Davis and KIBA, and revealed superior performance of DrugForm-DTA with best result for KIBA (MSE=0.117). Moreover, we developed a ready-to-use model using BindingDB dataset that was subjected to high-quality filtering and transformation. Overall, our method predicts drug-target affinity values with a confidence level comparable to a single in-vitro experiment. Also, we compared DrugForm-DTA against molecular modeling methods and revealed higher efficacy of the developed model for drug-target affinity predictions. Our investigation provides a high accuracy neural network model with performance comparable to experi-mental measurements, filtered and reassessed BindingDB dataset for further usage, and demonstrates outstanding applicability of the proposed method for DTA prediction. Predicting drug-target binding affinity is a crucial task in computational drug discovery. Here we present DrugForm-DTA, a ready-to-use Transformer-based model which requires only protein amino acid sequence and ligand SMILES as inputs. The model shows excellent results at commonly used benchmarks. We believe that the success in solving machine learning tasks depends on data quality and quantity, so the main focus of our work is the training data, but not a sophisticated neural architecture. We used BindingDB database as the source of experimental affinity measurements and prepared a refined and purified dataset, suitable for training machine learning models. Trained on this dataset, the DrugForm-DTA model demonstrates accuracy, comparable to a single in-vitro experiment. We also reveal that our model outperforms molecular modeling methods in estimating binding affinity. Both the prepared dataset and the trained model is published and freely available.