Docking and QSAR of Aminothioureas at the SARS-Cov-2 S-Protein–Human ACE2 Receptor Interface

molecules

Article Docking and QSAR of Aminothioureas at the SARS-CoV-2 S-Protein–Human ACE2 Receptor Interface

Wojciech Płonka 1,2 , Agata Paneth 3 and Piotr Paneth 4,5,* 1 Center for Bioinformatics (ZBH), Universität Hamburg, 20146 Hamburg, Germany; [email protected] 2 FQS-Fujitsu Poland, Parkowa 11, 33-332 Kraków, Poland 3 Department of Organic Chemistry, Faculty of Pharmacy, Medical University of Lublin, Chod´zki4a, 20-093 Lublin, Poland; [email protected] 4 Institute of Applied Radiation Chemistry, Faculty of Chemistry, Lodz University of Technology, Zeromskiego˙ 116, 90-924 Lodz, Poland 5 International Center for Research on Innovative Biobased Materials (ICRI-BioM)—International Research Agenda, Lodz University of Technology, Zeromskiego˙ 116, 90-924 Lodz, Poland * Correspondence: [email protected]; Tel.: +48-42631-3199

Academic Editor: Alla P. Toropova Received: 11 September 2020; Accepted: 7 October 2020; Published: 12 October 2020 Abstract: Docking of over 160 aminothiourea derivatives at the SARS-CoV-2 S-protein–human ACE2 receptor interface, whose structure became available recently, has been evaluated for its complex stabilizing potency and subsequently subjected to quantitative structure–activity relationship (QSAR) analysis. The structural variety of the studied compounds, that include 3 different forms of the N–N–C(S)–N skeleton and combinations of 13 different substituents alongside the extensive length of the interface, resulted in the failure of the QSAR analysis, since different molecules were binding to different parts of the interface. Subsequently, absorption, distribution, metabolism, and excretion (ADME) analysis on all studied compounds, followed by a toxicity analysis using statistical models for selected compounds, was carried out to evaluate their potential use as lead compounds for drug design. Combined, these studies highlighted two molecules among the studied compounds, i.e., 5-(pyrrol-2-yl)-2-(2-methoxyphenylamino)-1,3,4-thiadiazole and 1-(cyclopentanoyl)-4-(3-iodophenyl)-thiosemicarbazide, as the best candidates for the development of future drugs.

Keywords: SARS-CoV-2; aminothioureas; docking; QSAR; ADMET

1. Introduction With the outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [1] devastating pandemic that has already claimed the lives of over one million people worldwide [2], therapeutics are urgently needed. For future prevention, a few vaccine strategies are being tested. At the same time, medicines that can be used in ﬁghting the infection are being sought. It is thus not surprising that vigorous research is being carried out worldwide. One of the targets of both types of studies is the interface between the virus S-protein and the ACE2 receptor, focused on complex stabilizers and protein–protein binding inhibitors. Recent studies in this area include experimental [3,4] and theoretical [5–8] structural analyses, as well as mutagenic studies [9]. Advances from all these studies have been summarized [10]. The family of coronaviruses is relatively well known due to the previous severe human epidemics of SARS [11] and Middle East respiratory syndrome (MERS) [12]. In the search for medicines instantly available, repurposing of already approved drugs is studied as the ﬁrst line of drug

Molecules 2020, 25, 4645; doi:10.3390/molecules25204645 www.mdpi.com/journal/molecules Molecules 2020, 25, 4645 2 of 14 research. This approach has been attempted during the MERS epidemic [13] and is also being tried currently [14–18]. In the first of these studies, an NIH library of over 700 compounds was screened experimentally, identifying around 80 molecules with an anti-coronavirus effect. The most extensive study [19] concentrated on the interaction of the spike protein (S-protein) of the virus with the human ACE2 receptor, which is thought to be responsible for viral recognition by the host cells. By combining virtual high-throughput screening with ensemble docking of over 9000 compounds from the SWEETLAND library [20] and by using SARS-CoV-2 S-protein (NCBI: YP_009724390.1) and human ACE2 receptor (PDB: 2AJF) as templates to generate the SARS-CoV-2 S-protein–ACE2 receptor complex model, around 80 compounds already in clinical use that should exhibit anti-coronavirus activity have been identified. The S-protein–ACE2 receptor interface is continuing to be a subject of vigorous structural [3,4] and mutagenic [9] experimental studies, as well as of theoretical assessments [5–7,21]. The implications of these studies for therapy have been summarized recently [10]. While repurposing of compounds currently in medical use is necessary for a quick response to this public health threat, in the long run, drugs specific to this particular virus will be needed. With this aim in mind, we used the SARS-CoV-2 S-protein–ACE2 receptor complex model and its components to evaluate the binding properties of thiosemicarbazides, thiadiazoles, and triazoles that we previously studied for their anti-Toxoplasma gondi [22–25], antiviral [26], antibacterial [27–32], anticancer [17,33–35], anticonvulsant [36–38], analgesic [39,40] activities. Docking studies allowed the comparison of the binding affinities of the considered compounds to the S-protein and ACE2 receptor interface. Subsequently, we carried out a quantitative structure–activity relationship (QSAR) analysis in the attempt to predict the binding properties of other compounds in the studied class. Unfortunately, the QSAR analysis failed to provide the expected guidance for this purpose. The probable reasons for this failure are discussed. We, therefore, carried out an absorption, distribution, metabolism, and excretion (ADMET) analysis (with a toxicity analysis restricted to the most promising lead candidates). Combined, our results point to two molecules among the studied compounds, i.e., 5-(pyrrol-2-yl)-2-(2-methoxyphenylamino)-1,3,4-thiadiazole and 1-(cyclopentanoyl)-4-(3-iodophenyl)-thiosemicarbazide, as the best candidates for the development of future drugs.

2. Results The main focus of the present study was on the binding of selected molecules to the SARS-CoV-2 S-protein–human ACE2 receptor interface, although binding to the individual S-protein and ACE2 receptor was also analyzed. Three classes of compounds containing an N–N–C(S)–N skeleton, in a linear or cyclic topology, used in our laboratory in recent years for their inhibitory activity against several enzymes, were studied. Their structures contain three main cores: a linear carbonylthiosemicarbazide or its two cyclic derivatives, 1,3,4-thiadiazole and 1,2,4-triazole, each decorated with two substituents. As the C-substituent, one of the four ﬁve-member rings was used, while the N-substituent was a benzene ring or its ortho, meta, or para mono-substituted derivatives. These components are listed in Table1, where also partial codes for the di ﬀerent moieties are provided (in bold). Thus, for example, the compound code FSoOH corresponds to 5-(2-methylfuran-3-yl)-2-(2-hydroxyphenylamino)-1,3,4-thiadiazole, illustrated in Figure1 (this turned out to be the best ligand from among the studied compounds, as shown below). In total, 166 of the above compounds were considered. Molecules 2020, 25, x 3 of 14 Molecules 2020, 25, x 3 of 14 MoleculesMolecules2020Molecules, 25 20, 464520 ,20 2520, x, 25, x 3 of 14 3 of 143 of 14 MoleculesMolecules 20 2020,20 25, ,2 x5 , x 3 of3 14of 14 MoleculesMolecules 2020, 2 520, x20 , 25, x 3 of 14 3 of 14

Figure 1. Chemical structure of the compound FSoOH . Figure 1. Chemical structure of the compound FSoOH .. FigureFigureFigure 1. Chemical Figure1. Chemical1. Chemical 1. structure Chemical structure structure of structure the of compoundofthe the compound of compound the compoundFSoOH FSoOH FSoOH. FSoOH. . . FigureFigure 1. Chemical 1. Chemical structure structure of the compoundof the compound FSoOH FSoOH. . Table 1. Lists of structural fragments of the compounds used in the current study. Table 1.TableLists 1. of Lists structural of structural fragments fragments of the compoundsof thethe compounds used in used the currentin thethe current study. study.. TableTable 1.Table Lists1. Lists 1. of Lists ofstructural structural of structural fragments fragments fragments of ofthe the compounds of compounds the compounds used used in used inthe the current in current the current stud study. ystud. y. CTable-substituents Table1. Lists 1. of RLists 1structural of structural fragments fragmentsC oreof the compoundsof the compounds used in used the currentin theN -currentsubstitutents study. study. R2 C-substituents R11 Core N-substitutents2 R22 C-substituentsC-substituents1 RR 1 oreCore C N-substitutentsN-substitutents R2 R 2 C-substituentsC-substituentsCH R 1 R CoreC ore N-substitutentsN-substitutents R 2 R C-substituents1 3 R Core N-substitutents2 R C-substituentsCH R3 Core N-substitutents R CHCH3CH3 CH C3H3 3 O H H O H H O O O OH H HH H H O O O O H H HN H N O O 1 N N 2 O O 1 R NN N N 2 R 1 1 N N N N 2N 2 2-methylfuran -3-yl (F)R 1 N N N R2 2-methylfuran-3 -yl (F)1 R R RNNN N N 2 R R R 2-methylfuran2-methylfuran CH-3-yl - R(3F-yl) (FR) N N H RS R 2-methylfuran22-methylfuran-3-yl-methylfuranCH-3--yl33- yl(F )( F) 2-methylfuran-C3-Hyl3 C(FH) H H S S CHC3H3 3 carbonylthiosemicarbazideH H S S CH3 3 carbonylthiosemicarbazideH S carbonylthiosemicarbazidecarbonylthiosemicarbazidecarbonylthiosemicarbazidecarbonylthiosemicarbazide(C) (C ) N carbonylthiosemicarbazidecarbonylthiosemicarbazide((C)) N (C)( C) (C) H N N N (C) (C) H N N N 1 S H H H N 1 R S H H N N 1 1 S 2 N N N R1 S S S N 2 3 N H 1 R R R S N N N2 R 3 N H R S N 2 2 3 R H H R N R2 3R3 3 4-methylH-Himidazol -5-yl N N2 R R R R3 = H, ortho, meta, or3 pRara: F3, ClR, Br, I, OH, OMe, 4--methylH--imidazolimidazol --5--yl N N R 33 R N RN R = H3,, ortho,, meta,, oror paraR :: F,, Cl,, Br,, I,, OH,, OMe,, 4-methyl4-methyl4-methyl-imidazol-imidazol-imidazol(I-)5 --yl5- yl- 5-yl N N1,3,41,3,4-thiadiazoleN -Nthiadiazole ( S) 3 3 R =3 H, ortho, meta, or p ara: F, Cl, Br, I, OH, OMe, 4-methyl-imidazol-5-yl4-methyl-imidazol(I) -5-yl (I ) 1,3,4N-thiadiazoleN (S) R R=3 H =, HoRrtho, ortho=, mH,eta ,morthoeta, or, ,orpmetaara para: Me,F or,: ClF,para ,NO ,Cl Br,: 2,Br FI,,, Cl OHI, ,OH, OMe, OMe, , 4-methyl-imidazol(I)- 5-yl N 1,3,4N-thiadiazole2 (S) R3 = H,R o rtho= H,, morthoeta, ,or m etapara, or:Me F p, Clara,, NO,: BrF2,2 , Cl I, ,OH Br,, IOMe, OH, OMe, (I) (I) (I) 1,3,4-thiadiazole1,3,4-thiadiazole (S) (S) 2 (I) 1,3,4-thiadiazole2 R (S) Br, I, OHMe, OMeMe, NO, MeNO, 2Me ,2 NO, NO 2 (I) 1,3,4-thiadiazole (S) R2 Me, NO2 2R2 Me, NO2 2 R R R N R 1 R N N 1 R N S N 1R1 1 N S N N H R1 RN N S S N H 1 R R N S S H R S pyrrolH H-2 -ylH (P) N N pyrrolpyrrol-2-ylH --2--ylH ( P)) N N pyrrolpyrrol-pyrrol2--yl2 - yl(P- )(2 P-yl) (P) N N NNN N pyrrol-pyrrol2-yl (P-2) -yl (P) N NN N H H H 1,2,41,2,4-triazole-3-thione-triazoleH -H3-thione ( T) 1,2,4--triazoletriazoleH--3--thionethione ( (T)) cyclopentyl (C) 1,2,41,2,4-triazole1,2,4-triazole-triazole-3--thione3-thione-3- (thioneT )( T) (T) cyclopentyl ( C )) 1,2,4-triazole1,2,4-triazole-3-thione-3- thione(T) (T) cyclopentylcyclopentylcyclopentylThe (C d )(ocking C) (C) results for all compounds are available in the Supplementary Material for the cyclopentylcyclopentylThe ( dCocking) (C) results for all compounds are available inin thethe Su Supplementary Material for the TheinterfaceThe d ockingThe docking between docking results results the results for forS - allprotein allfor compounds compounds all of compounds the virus are are available and areavailable the available humanin inthe the in SuACE2 the Supplementaryp plementary Su receptorpplementary Material(as Material well Material for for for these the thefor two the Theinterfaceinterface dTheocking between dbetweenocking results resultsthethe for SS - allprotein for compounds all of compounds the virus are available and are the available humanin the in SuACE2 thepplementary Su receptorpplementary (as Material well Material for for thesethese the for twotwo the interfaceTheinterface dockingproteinsinterface between between resultsseparately).between the the forS- proteinSthe- all proteinIn S compoundsTable-protein of of the2 ,the compounds ofvirus virusthe are andvirus available and the with andthe human humanthe in thebesthuman ACE2 SupplementaryACE2 docking ACE2receptor receptor scor receptor (ases Material(asfor well well(asboth for well forthefor these theseforinterface the twothese two andtwo interfaceproteinsinterface between separately).between the S -theprotein In S -Tableprotein of the2,, compoundsof virus the virusand the withand human thethe humanbest ACE2 docking ACE2 receptor scor receptor es(as for well (asboth forwell thethe these forinterfaceinterface thesetwo andtwoand interfaceproteinsproteins betweentheproteins separately). individual separately). theseparately). 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Molecules 2020, 25, 4645 4 of 14 Molecules 2020, 25, x 4 of 14

Descriptor set RF One molecule per leaf RF Two molecules per leaf

MOE All

MOE 15

Morgan

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FigureFigure 2. Results 2. Results of the of evaluation the evaluation of Random of Random Forest Forest Regressor Regressor models models on the on training the training set (blue set (blue points) and leave-one-outpoints) and leave cross-validation-one-out cross-validation (orange points) (orange for points) various for various considered considered descriptor descriptor sets. sets.

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Descriptor set RF One molecule per leaf RF Two molecules per leaf

MOE All

MOE 15

Morgan

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Figure 3. Diagnostic plots for the Random Forest Regressor models on the training set (blue points) and leave-one-out cross-validation (orange points) for various considered descriptor sets.

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Table 2. ChemPLP docking scores of the studied compounds in relation to S-protein, ACE2 receptor, Table 2. ChemPLP docking scores of the studied compounds in relation to S-protein, ACE2 receptor, and their interface and toxicity results for statistical bacteria, mammals, and fish models. and their interface and toxicity results for statistical bacteria, mammals, and ﬁsh models. Human Carcino Daphnia Test Virus S- Human TA100/TA1535 Carcino Daphnia Compound Interface Virus ACE2 TA100/TA1535 (Mouse) (vs. Compound Interface Protein ACE2 Ames Tests (Mouse) Test (vs. S-Protein Receptor Ames Tests Test Chloroquine) Receptor Test Chloroquine) FSoOH 54.71 39.89 53.16 −+++ negative 3.0 FSoOH 54.71 39.89 53.16 +++ negative 3.0 PSoF 53.96 49.01 47.68 all positive− positive 1.8 PSoF 53.96 49.01 47.68 all positive positive 1.8 FCmOHFCmOH 53.6753.67 46.0746.07 51.68 51.68 −++++++ negativenegative 3.3 3.3 − PSoMePSoMe 52.0952.09 49.0549.05 47.45 47.45 +−−++ + negativenegative 1.3 1.3 −− CCmICCmI 53.2553.25 41.24 41.24 52.6 52.6 ++−++++ negativenegative 0.9 0.9 − FCpFFCpF 46.4846.48 49.0149.01 45.56 45.56all all positive positive positivepositive 2.5 2.5

3.3. Discussion Discussion AsAs can can be be seen seen from from thethe dockingdocking results presented in in Table Table 22,, FSoOHFSoOH yieldedyielded the the best best result resultss for forthe the interface interface as as well well as as for for the the human human ACE2 receptor.receptor. The position position of of its its best best binding binding pose pose is is illustratedillustrated in in Figure Figure4. This 4. This molecule molecule binds binds strongly strongly to the to Leu29–His34 the Leu29–His34 fragment fragment of the terminalof the terminal helix ofhelix the ACE of the receptor ACE receptor and, to a and lesser, to extent,a lesser to exten the Pro49–Tyr495t, to the Pro49 fragment–Tyr495 offragment the S-protein. of the S Among-protein. theAmong N–N–C(S)–N the N–N motifs,–C(S)–N the motif thiadiazoles, the thiadiazole ring is the ring moiety is the most moiety frequently most frequent presently in present top binding in top compounds.binding compounds. In this group, In this substituents group, substituents attached to attached the nitrogen to the atom nitrogen are most atom frequently are most substituted frequently insubstituted the ortho position in the ortho by ﬂuorine position or aby hydroxyl fluorine group.or a hydroxyl Furan is group. the substituent Furan is the of choice substituent on the of carbon choice side.on theIt is worthcarbon noticing side. It that is worth the compound noticing CCmI that the(together compound with ICpNOCCmI 2(togetherand CCpNO with2) ICpNO was shown2 and notCCpNO to be cytotoxic2) was shown in our not recent to be studies cytotoxic on anti-in ourT. gondiirecentactivity studies (unpublishedon anti-T. gondii results). activity For (unpublished this ligand, theresults). molecular For this interactions ligand, the in themolecular binding interactions groove are illustratedin the binding in Figure groove5. are illustrated in Figure 5.

FigureFigure 4. 4.The The best best binding binding pose pose of ofFSoOH FSoOHat at the the virus virus spike spike protein protein (S-protein, (S-protein green)–human, green)–human ACE2 ACE2 receptorreceptor (orange) (orange) interface. interface.

InIn an an attempt attempt to to extract extract the the knowledge knowledge required required for for the the design design and and synthesis synthesis of of new new compounds compounds similarsimilar to to those those in in our our set set but but with with improved improved activity, activity, we we used used Machine Machine Learning Learning and and performedperformed a aQSAR QSAR analysis of of the the S-protein–ACE2 S-protein–ACE2 interface interface docking docking scores. scores. We We wanted wanted to to investigate investigate the the relationshipsrelationships between between activity activity and and general general physicochemical physicochemical properties properties of of the the compounds, compounds, general general structuralstructural features, features, as as well well as as features features speciﬁc specific to to our our molecules. molecules. We We employed employed similar similar techniques techniques used used inin the the past past [41 [41,,4242], which], which proved proved successful. successful. We We chose chose the the Random Random Forest Forest Regressor Regressor as as a modelinga modeling algorithm,algorithm, due due to to its its ability ability to to handle handle non-linear non-linear relations relations and and the the possibility possibility of of learning learning from from a small a small datasetdataset with with a largea large number number of of features. features.

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Figure 5. Interactions of CCmI in the binding groove of the interface. Figure 5. Interactions of CCmI in the binding groove of the interface. It can be observed that Random Forests with one molecule per leaf described the training set betterIt than can thosebe observed with two that molecules Random perForests leaf. with General one physicochemical molecule per leaf and describe topologicald the descriptorstraining set (MOEbetter All,than MOE those 15) with worked two molecules better than per fingerprints, leaf. General while physicochemical counts of fragments and topological characteristic descriptors to the set(MOE performed All, MOE poorly. 15) work Thised suggests better than that fingerprints, general shape-related while counts and of physicochemicalfragments characteristic properties to the of moleculesset perform areed more poorly important. This suggests for activity that than general particular shape substituents-related and present physicochemical in our set of properties compounds. of molecules are more important for activity than particular substituents present in our set of It should also be noted that there was almost no difference in the behavior of the models built with 354compounds MOE 2D. descriptorsIt should also and be a noted limited that set there of 15, wa confirmings almost no the difference Random in Forests’ the behavior capability of the to models handle built with 354 MOE 2D descriptors and a limited set of 15, confirming the Random Forests’ capability feature selection issues automatically. The leave-one-out cross-validation Rˆ2 of about 0.4 of the models, however,to handle didfeature not encourageselection issues their specificautomatically. use for Th judginge leave the-one potency-out cross of new-validation compounds; R^2 of evidently,about 0.4 of the models, however, did not encourage their specific use for judging the potency of new for this purpose, more training data are needed. compoundsThis somewhat; evidently disappointing, for this purpose performance, more training of the data QSAR are needed. analysis may have several causes. This somewhat disappointing performance of the QSAR analysis may have several causes. Firstly, the number of studied molecules and their structural variety could have been too limited. Firstly, the number of studied molecules and their structural variety could have been too limited. However, since our previous experience with a similar technique using much smaller sets was quite However, since our previous experience with a similar technique using much smaller sets was quite positive [24,25] we think the most important reason was the size of the interface. This spans for nearly positive [24,25] we think the most important reason was the size of the interface. This spans for nearly 45 Å in contrast to the quite confined size of a typical active site of an enzyme. This has several 45 Å in contrast to the quite confined size of a typical active site of an enzyme. This has several consequences. One of them is the fact that different molecules can bind to different parts of the interface consequences. One of them is the fact that different molecules can bind to different parts of the groove, steered by forces (van der Waals, hydrogen bonding, electrostatic interactions, etc.) in different interface groove, steered by forces (van der Waals, hydrogen bonding, electrostatic interactions, etc.) combinations. This is illustrated in Figure6, which presents an overlay of the best 10 binding poses for in different combinations. This is illustrated in Figure 6, which presents an overlay of the best 10 each of the studied molecules. binding poses for each of the studied molecules. In the absence of clear indicative QSAR results that would allow us to scrutinize compounds In the absence of clear indicative QSAR results that would allow us to scrutinize compounds outside the studied set, we analyzed the ADMET properties of the compounds used in the docking outside the studied set, we analyzed the ADMET properties of the compounds used in the docking and QSAR studies, in search for the best lead candidates within this set. The ADME analysis and QSAR studies, in search for the best lead candidates within this set. The ADME analysis comprised the evaluation of physicochemical parameters, druglikeness, lipophilicity, pharmacokinetics, comprised the evaluation of physicochemical parameters, druglikeness, lipophilicity, and leadlikeness. A typical collection of consensus parameters for a given compound is illustrated pharmacokinetics, and leadlikeness. A typical collection of consensus parameters for a given in the left panel of Figure7 for CCmI, while all individual values are presented in the Supporting compound is illustrated in the left panel of Figure 7 for CCmI, while all individual values are Information (Table S5). The right panel of Figure7 illustrates a comparison of the results obtained presented in the Supporting Information (Table S5). The right panel of Figure 7 illustrates a for all comperrorsounds. As can be seen, with a few exceptions, all points lie in the white area of the comparison of the results obtained for all comperrorsounds. As can be seen, with a few exceptions, graph, relating logP to polar surface area (TPSA), indicating good oral bioavailability. all points lie in the white area of the graph, relating logP to polar surface area (TPSA), indicating good oral bioavailability.

Molecules 2020, 25, 4645 8 of 14 Molecules 2020, 25, x 8 of 14

Molecules 2020, 25, x 8 of 14

FigureFigure 6. 6. 6. Overlay OverlayOverlay of of the ofthe the10 10 bestbest 10 bestbinding binding posesposes of posesof all all studied studied of all compounds studied compounds compounds in inthe the groove groove in of the theof groovethe S-pro S-protein oftein– the– S-protein–ACE2ACE2ACE2 receptor receptor interface interface receptor (for(for interface colorcolor code (for, color, seesee Figure Figure code, 4) see 4). . Figure4).

Figure 7. Results of the absorption, distribution, metabolism, and excretion (ADME) assessment. Figure 7. Results of the absorption, distribution, metabolism, and excretion (ADME) assessment. Left Left panel: consensus values for a single compound (CCmI). Right panel: BOILED-Egg graph [43] Figurepanel: 7. consensus Results of valuesthe absorption, for a single distribution, compound metabolism, (CCmI). Right and excretionpanel: BOILED (ADME-Egg) assessment. graph [43 Left] illustrating good gastrointestinal absorption (white area). panel:illustrating consensus good gastrointestinal values for a singleabsorption compound (white area). (CCmI ). Right panel: BOILED-Egg graph [43] Sinceillustrating the ADME good gastrointestinal analysis also absorption turned out (white not toarea). be signiﬁcantly discriminative for the studied Since the ADME analysis also turned out not to be significantly discriminative for the studied compounds, we have carried out toxicity studies using statistical models. These calculations were compoundsSince the, weADME have analysis carried outalso toxicity turned studies out not using to be statistical significantly models. discriminat These calculationsive for the werestudied compounds, we have carried out toxicity studies using statistical models. These calculations were

Molecules 2020, 25, 4645 9 of 14 performed only for the compounds listed in Table2. The results of the tests are given in the last three columns of this table. The last column reports values relative to those obtained for chloroquine, a medicine considered for repurposed used against coronavirus disease 2019 (COVID-19), which, with the ChemPLP score of 66.29, exhibits strong complex-stabilization properties. As can be seen, toxicity results advocate against considering the compounds PSoF and FCpF, while favoring PSoMe and CCmI in further drug development studies.

4. Materials and Methods

4.1. Docking We used the published model [19] of the interface between the viral S-protein and the human ACE2 receptor that was constructed using SARS-CoV-2 S-protein (NCBI: YP_009724390.1) and human ACE2 receptor (PDB: 2AJF) as templates. The structure of this model has been recently confirmed experimentally [44]. A few docking algorithms have been tested. Initially, following the literature data, Vina [45] docking program was used. However, the scores obtained for the strongest inhibitors were very close and not discriminating. We, therefore, switched to the FlexX algorithm [46], as implemented in the LeadIT platform [47]. The scoring function of this program did a better job in the differentiation of the binding affinities of the studied compounds but showed some constraints, the most serious being the limit on the size of the acceptor, which precluded, in some cases, searches of the whole enzyme. SwissDock [48] in our hands also turned out not practical, as only a single ligand per submission to the server was possible. We therefore finally decided to continue with the ChemPLP algorithm [49] as implemented in the Gold program [50]. It is also noteworthy that this algorithm has been evaluated as one of the best in the most recent benchmark studies [51]. For the preparation and visualization of proteins and ligands, apart from those embedded in the docking programs, Hyperchem [52], Gaussview [53], Chimera [54], and Mercury [55] were used.

4.2. QSAR Four sets of diﬀerent molecular descriptors were used:

All MOE [56] descriptors, as a measure of general properties, see Table S3 • 15 MOE 2D descriptors selected by Particle Swarm Optimization using Fujitsu ADMEWORKS • ModelBuilder software [57] (‘“ASA_H”, “a_acc”, “a_nH”, “a_nN”, “E_ele”, “E_oop”, “GCUT_PEOE_3”, “GCUT_SLOGP_1”, “opr_violation”, “PEOE_VSA_PPOS”, “rsynth”, “SlogP_VSA6”, “SMR_VSA1”, “vsurf_CW5”, “vsurf_W5”), which were found to correlate best with activity in a linear combination, as a low-count set of general properties Morgan Fingerprints [58] with Radius = 3 and bit length of 4096 as general structural features • Counts of fragments frequently appearing in our compound set obtained by the RECAP algorithm • using Fujitsu ADMEWORKS ModelBuilder software, as features most speciﬁc to our set, see Table S4

Random Forest Regressor has been used as a modeling algorithm. The Rˆ2 on the whole training set and Rˆ2 of the leave-one-out cross-validation were used as metrics for learning and prediction performance, respectively. Calculations were done using Python scripts in the Anaconda environment [59] and the Fujitsu ADMEWORKS ModelBuilder software. Models were built using scikit-learn [60] of Random Forest Regressor with default parameters, except min_samples_leaf, as described in the Results section, and ﬁxed random seed for reproducibility.

4.3. ADMET The SwissADME program [61] implemented online [62] was used for the assessment of the ADME properties of all the studied compounds. The BOILED-Egg graph, which uses logP calculated by the Wildman and Crippen method [63] and TPSA [64], was generated at the same site. Molecules 2020, 25, 4645 10 of 14

Molecules 2020, 25, x 10 of 14 Toxicology studies were carried out using online implementation [65] of the PreADMET programTA1535_10RLI/TA1535_NA [66]. From among available tests on statistical Salmonella models, typhimurium Ames TA100_10RLI bacterial strain/TA100_NA [67] and/TA1535_10RLI mouse [68]/ TA1535_NAcarcinogenicity, tests and on DSalmonellaaphnia fish typhimurium[69] toxicity werebacterial carried strain out. [67 ] and mouse [68] carcinogenicity, and Daphnia fish [69] toxicity were carried out. 5. Conclusions 5. Conclusions The presented docking studies identified 5-(2-methylfuran-3-yl)-2-(2-hydroxyphenylamino)- 1,3,4-Thethiadiazole presented as dockingthe molecule studies with identified the best 5-(2-methylfuran-3-yl)-2-(2-hydroxyphenylamino)-1,3,4- docking score among the studied set of nearly 170 thiadiazolecompounds as containing the molecule the with –N– theN– bestC(S) docking–N– motif. score The among resulting the studied scores, set when of nearly subjected 170 compounds to QSAR containinganalysis did the not –N–N–C(S)–N–, however, yield motif. a model The useful resulting for scores,a rational when search subjected of other to QSARrelated analysis molecules. did The not, however,most probable yield reasona model for useful this isfor the a rational length of search the interface of other relatedbinding molecules. groove, resulting The most in probabledifferent reasonbinding for forces this depending is the length on ofthe the exact interface site of the binding best interactions groove, resulting between in proteins different and binding ligands. forces dependingAs a side on theeffect exact of our site calculations of the best interactions, yet another between warning proteins for the and type ligands. of studies presented here can beAs indicated. a side effect Attempts of our calculations, are being made yet another to use warning massive for doc theking type analys of studieses for presented large libraries here can of beligands indicated. in rational Attempts drug are design being ( madesee for to example use massive, reference docking 11 analyses for studies for largeon the libraries S-protein of ligands–ACE2 inreceptor rational interface drug design.) These (see studies for example, are excessively reference automated 11 for studies, leading on to the thousands S-protein–ACE2 of poses receptorthat can interface.)hardly be reviewed These studies manually are excessively, as was done automated, in this contribution. leading to However, thousands as of depicted poses that in Figure can hardly 8 for bethereviewed compound manually, PSmMe, as the was ligand done can in thisbind contribution. several neighboring However, sites. as In depicted fact, inin this Figure case8, bindingfor the compoundinside the ACE2PSmMe receptor, the ligand (two poses can bind on the several left side) neighboring exhibited sites. much In fact,higher in thisscores case, (57.22 binding and inside59.22) thethan ACE2 binding receptor at the (two S-protein poses–ACE2 on the receptor left side) interface. exhibited This much example higher illustrates scores (57.22 possible and 59.22) pitfalls than of bindingautomatic at thedocking S-protein–ACE2 and advocates receptor for interface.a rectangular This exampledefinition illustrates of the docking possible space pitfalls (available of automatic, for dockingexample and, in Vina) advocates rather for than a rectangular for the more definition popular of thedocking docking within space a (available,given radius for from example, a molecule, in Vina) ratherresidue, than or point. for the more popular docking within a given radius from a molecule, residue, or point.

Figure 8.8. OverlayOverlay ofof the the 10 10 best best binding binding poses poses of PSmMeof PSmMecompound compound in the in groovethe groove of the of S-protein–ACE2 the S-protein– receptorACE2 receptor interface interface (for color (for code color see code Figure see 4Figure). 4).

Molecules 2020, 25, 4645 11 of 14

Overall, our combined docking, QSAR, and ADMET studies led us to the conclusion that two of compounds among the studied aminothioureas, with high, although not the highest, docking scores are suitable for further search for a drug against COVID-19 due to their predicted low toxicity. These molecules are 5-(pyrrol-2-yl)-2-(2-methoxyphenylamino)-1,3,4-thiadiazole (PSoMe) and 1-(cyclopentanoyl)-4-(3-iodophenyl)-thiosemicarbazide (CCmI). The latter showed in the Daphnia toxicity test results even better than those of chloroquine, which is used in medical practice. We conﬁrmed its low toxicity experimentally also in other studies.

Supplementary Materials: The following are available online, Docking scores obtained for all studied compounds, MOE descriptors, list of substructures generated by the RECAP algorithm used as counts in QSAR modeling, and results of ADME calculations can be found at https://doi.org/10.6084/m9.figshare.12130932.v3. Author Contributions: W.P. carried out QSAR calculations. A.P. and P.P. carried out ADMET and docking calculations. All authors were involved in discussion of the results and writing and proofreading the manuscript. All authors have read and agreed to the published version of the manuscript. Funding: This research was partly supported by the Foundation for Polish Science via the grant MAB PLUS/2019/11. Conflicts of Interest: The authors declare no conflict of interest.

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Sample Availability: Samples of the compounds are not available from the authors.

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