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The development of SARS-CoV-2 main protease (M pro ) inhibitors for the treatment of COVID-19 has mostly benefitted from X-ray structures and preexisting knowledge of inhibitors; however, an efficient method to generate M pro inhibitors, which circumvents such information would be advantageous. As an alternative approach, we show here that DNA-encoded chemistry technology (DEC-Tec) can be used to discover inhibitors of M pro . An affinity selection of a 4-billion-membered DNA-encoded chemical library (DECL) using M pro as bait produces novel non-covalent and non-peptide-based small molecule inhibitors of M pro with low nanomolar K i values. Furthermore, these compounds demonstrate efficacy against mutant forms of M pro that have shown resistance to the standard-of-care drug nirmatrelvir. Overall, this work demonstrates that DEC-Tec can efficiently generate novel and potent inhibitors without preliminary chemical or structural information., (© 2023. Springer Nature Limited.)

BRDT, BRD2, BRD3, and BRD4 comprise the bromodomain and extraterminal (BET) subfamily which contain two similar tandem bromodomains (BD1 and BD2). Selective BD1 inhibition phenocopies effects of tandem BET BD inhibition both in cancer models and, as we and others have reported of BRDT, in the testes. To find novel BET BD1 binders, we screened >4.5 billion molecules from our DNA-encoded chemical libraries with BRDT-BD1 or BRDT-BD2 proteins in parallel. A compound series enriched only by BRDT-BD1 was resynthesized off-DNA, uncovering a potent chiral compound, CDD-724, with >2,000-fold selectivity for inhibiting BRDT-BD1 over BRDT-BD2. CDD-724 stereoisomers exhibited remarkable differences in inhibiting BRDT-BD1, with the R-enantiomer (CDD-787) being 50-fold more potent than the S-enantiomer (CDD-786). From structure–activity relationship studies, we produced CDD-956, which maintained picomolar BET BD1 binding potency and high selectivity over BET BD2 proteins and had improved stability in human liver microsomes over CDD-787. BROMOscan profiling confirmed the excellent pan-BET BD1 affinity and selectivity of CDD-787 and CDD-956 on BD1 versus BD2 and all other BD-containing proteins. A cocrystal structure of BRDT-BD1 bound with CDD-956 was determined at 1.82 Å and revealed BRDT-BD1–specific contacts with the αZ and αC helices that explain the high affinity and selectivity for BET BD1 versus BD2. CDD-787 and CDD-956 maintain cellular BD1-selectivity in NanoBRET assays and show potent antileukemic activity in acute myeloid leukemia cell lines. These BET BD1-specific and highly potent compounds are structurally unique and provide insight into the importance of chirality to achieve BET specificity.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has killed more than 4 million humans globally, but there is no bona fide Food and Drug Administration-approved drug-like molecule to impede the COVID-19 pandemic. The sluggish pace of traditional therapeutic discovery is poorly suited to producing targeted treatments against rapidly evolving viruses. Here, we used an affinity-based screen of 4 billion DNA-encoded molecules en masse to identify a potent class of virus-specific inhibitors of the SARS-CoV-2 main protease (M pro ) without extensive and time-consuming medicinal chemistry. CDD-1714, the initial three-building-block screening hit (molecular weight [MW] = 542.5 g/mol), was a potent inhibitor (inhibition constant [ K i ] = 20 nM). CDD-1713, a smaller two-building-block analog (MW = 353.3 g/mol) of CDD-1714, is a reversible covalent inhibitor of M pro ( K i = 45 nM) that binds in the protease pocket, has specificity over human proteases, and shows in vitro efficacy in a SARS-CoV-2 infectivity model. Subsequently, key regions of CDD-1713 that were necessary for inhibitory activity were identified and a potent ( K i = 37 nM), smaller (MW = 323.4 g/mol), and metabolically more stable analog (CDD-1976) was generated. Thus, screening of DNA-encoded chemical libraries can accelerate the discovery of efficacious drug-like inhibitors of emerging viral disease targets., Competing Interests: Competing interest statement: A provisional patent involving the molecules described in this paper and their uses has been submitted., (Copyright © 2021 the Author(s). Published by PNAS.)

Small molecules that selectively bind to the pseudokinase JH2 domain over the JH1 kinase domain of JAK2 kinase are sought. Virtual screening led to the purchase of 17 compounds among which 9 were found to bind to V617F JAK2 JH2 with affinities of 40 - 300 μM in a fluorogenic assay. Ten analogues were then purchased yielding 9 additional active compounds. Aminoanilinyltriazine 22 was particularly notable as it shows no detectable binding to JAK2 JH1, and it has a 65-μM dissociation constant K d with V617F JAK2 JH2. A crystal structure for 22 in complex with wild-type JAK2 JH2 was obtained to elucidate the binding mode. Additional de novo design led to the synthesis of 19 analogues of 22 with the most potent being 33n with K d values of 2-3 μM for WT and V617F JAK2 JH2, and with 16-fold selectivity relative to binding with WT JAK2 JH1., Competing Interests: Declaration of competing Interest The authors declare no competing interests.

Bromodomain testis (BRDT), a member of the bromodomain and extraterminal (BET) subfamily that includes the cancer targets BRD2, BRD3, and BRD4, is a validated contraceptive target. All BET subfamily members have two tandem bromodomains (BD1 and BD2). Knockout mice lacking BRDT-BD1 or both bromodomains are infertile. Treatment of mice with JQ1, a BET BD1/BD2 nonselective inhibitor with the highest affinity for BRD4, disrupts spermatogenesis and reduces sperm number and motility. To assess the contribution of each BRDT bromodomain, we screened our collection of DNA-encoded chemical libraries for BRDT-BD1 and BRDT-BD2 binders. High-enrichment hits were identified and resynthesized off-DNA and examined for their ability to compete with JQ1 in BRDT and BRD4 bromodomain AlphaScreen assays. These studies identified CDD-1102 as a selective BRDT-BD2 inhibitor with low nanomolar potency and >1,000-fold selectivity over BRDT-BD1. Structure-activity relationship studies of CDD-1102 produced a series of additional BRDT-BD2/BRD4-BD2 selective inhibitors, including CDD-1302, a truncated analog of CDD-1102 with similar activity, and CDD-1349, an analog with sixfold selectivity for BRDT-BD2 versus BRD4-BD2. BROMOscan bromodomain profiling confirmed the great affinity and selectivity of CDD-1102 and CDD-1302 on all BET BD2 versus BD1 with the highest affinity for BRDT-BD2. Cocrystals of BRDT-BD2 with CDD-1102 and CDD-1302 were determined at 2.27 and 1.90 Å resolution, respectively, and revealed BRDT-BD2 specific contacts that explain the high affinity and selectivity of these compounds. These BD2-specific compounds and their binding to BRDT-BD2 are unique compared with recent reports and enable further evaluation of their nonhormonal contraceptive potential in vitro and in vivo., Competing Interests: Competing interest statement: A provisional patent involving the molecules in this paper and their uses has been submitted., (Copyright © 2021 the Author(s). Published by PNAS.)

Proteomic signatures associated with clinical measures of more aggressive cancers could yield molecular clues as to disease drivers. Here, utilizing the Clinical Proteomic Tumor Analysis Consortium (CPTAC) mass-spectrometry-based proteomics datasets, we defined differentially expressed proteins and mRNAs associated with higher grade or higher stage, for each of seven cancer types (breast, colon, lung adenocarcinoma, clear cell renal, ovarian, uterine, and pediatric glioma), representing 794 patients. Widespread differential patterns of total proteins and phosphoproteins involved some common patterns shared between different cancer types. More proteins were associated with higher grade than higher stage. Most proteomic signatures predicted patient survival in independent transcriptomic datasets. The proteomic grade signatures, in particular, involved DNA copy number alterations. Pathways of interest were enriched within the grade-associated proteins across multiple cancer types, including pathways of altered metabolism, Warburg-like effects, and translation factors. Proteomic grade correlations identified protein kinases having functional impact in vitro in uterine endometrial cancer cells, including MAP3K2, MASTL, and TTK. The protein-level grade and stage associations for all proteins profiled-along with corresponding information on phosphorylation, pathways, mRNA expression, and copy alterations-represent a resource for identifying new potential targets. Proteomic analyses are often concordant with corresponding transcriptomic analyses, but with notable exceptions.

DNA-encoded chemical libraries are collections of compounds individually coupled to unique DNA tags serving as amplifiable identification barcodes. By bridging split-and-pool combinatorial synthesis with the ligation of unique encoding DNA oligomers, million- to billion-member libraries can be synthesized for use in hundreds of healthcare target screens. Although structural diversity and desirable molecular property ranges generally guide DNA-encoded chemical library design, recent reports have highlighted the utility of focused DNA-encoded chemical libraries that are structurally biased for a class of protein targets. Herein, a protease-focused DNA-encoded chemical library was designed that utilizes chemotypes known to engage conserved catalytic protease residues. The three-cycle library features functional moieties such as guanidine, which interacts strongly with aspartate of the protease catalytic triad, as well as mild electrophiles such as sulfonamide, urea, and carbamate. We developed a DNA-compatible method for guanidinylation of amines and reduction of nitriles. Employing these optimized reactions, we constructed a 9.8-million-membered DNA-encoded chemical library. Affinity selection of the library with thrombin, a common protease, revealed a number of enriched features which ultimately led to the discovery of a 1 nM inhibitor of thrombin. Thus, structurally focused DNA-encoded chemical libraries have tremendous potential to find clinically useful high-affinity hits for the rapid discovery of drugs for targets (e.g., proteases) with essential functions in infectious diseases (e.g., severe acute respiratory syndrome coronavirus 2) and relevant healthcare conditions (e.g., male contraception)., Competing Interests: The authors declare no competing interest., (Copyright © 2020 the Author(s). Published by PNAS.)

Bacterial resistance to β-lactam antibiotics is largely mediated by β-lactamases, which catalyze the hydrolysis of these drugs and continue to emerge in response to antibiotic use. β-Lactamases that hydrolyze the last resort carbapenem class of β-lactam antibiotics (carbapenemases) are a growing global health threat. Inhibitors have been developed to prevent β-lactamase-mediated hydrolysis and restore the efficacy of these antibiotics. However, there are few inhibitors available for problematic carbapenemases such as oxacillinase-48 (OXA-48). A DNA-encoded chemical library approach was used to rapidly screen for compounds that bind and potentially inhibit OXA-48. Using this approach, a hit compound, CDD-97, was identified with submicromolar potency ( K i = 0.53 ± 0.08 μM) against OXA-48. X-ray crystallography showed that CDD-97 binds noncovalently in the active site of OXA-48. Synthesis and testing of derivatives of CDD-97 revealed structure-activity relationships and informed the design of a compound with a 2-fold increase in potency. CDD-97, however, synergizes poorly with β-lactam antibiotics to inhibit the growth of bacteria expressing OXA-48 due to poor accumulation into E. coli . Despite the low in vivo activity, CDD-97 provides new insights into OXA-48 inhibition and demonstrates the potential of using DNA-encoded chemistry technology to rapidly identify β-lactamase binders and to study β-lactamase inhibition, leading to clinically useful inhibitors.

DNA-encoded chemical library (DECL) screens are a rapid and economical tool to identify chemical starting points for drug discovery. As a robust transformation for drug discovery, palladium-catalyzed C-N coupling is a valuable synthetic method for the construction of DECL chemical matter; however, currently disclosed methods have only been demonstrated on DNA-attached (hetero)aromatic iodide and bromide electrophiles. We developed conditions utilizing an N -heterocyclic carbene-palladium catalyst that extends this reaction to the coupling of DNA-conjugated (hetero)aromatic chlorides with (hetero)aromatic and select aliphatic amine nucleophiles. In addition, we evaluated steric and electronic effects within this catalyst series, carried out a large substrate scope study on two representative (hetero)aryl bromides, and applied this newly developed method within the construction of a 63 million-membered DECL.

Methyltransferase 3 beta (DNMT3B) inhibitors that interfere with cancer growth are emerging possibilities for treatment of melanoma. Herein we identify small molecule inhibitors of DNMT3B starting from a homology model based on a DNMT3A crystal structure. Virtual screening by docking led to purchase of 15 compounds, among which 5 were found to inhibit the activity of DNMT3B with IC 50 values of 13-72 μM in a fluorogenic assay. Eight analogues of 7 , 10 , and 12 were purchased to provide 2 more active compounds. Compound 11 is particularly notable as it shows good selectivity with no inhibition of DNMT1 and 22 μM potency toward DNMT3B. Following additional de novo design, exploratory synthesis of 17 analogues of 11 delivered 5 additional inhibitors of DNMT3B with the most potent being 33h with an IC 50 of 8.0 μM. This result was well confirmed in an ultrahigh-performance liquid chromatography (UHPLC)-based analytical assay, which yielded an IC 50 of 4.8 μM. Structure-activity data are rationalized based on computed structures for DNMT3B complexes., Competing Interests: The authors declare no competing financial interest., (Copyright © 2020 American Chemical Society.)

A strategy for DNA-compatible, palladium-catalyzed hydroxycarbonylation of (hetero)aryl halides on DNA-chemical conjugates has been developed. This method generally provided the corresponding carboxylic acids in moderate to very good conversions for (hetero)aryl iodides and bromides, and in poor to moderate conversions for (hetero)aryl chlorides. These conditions were further validated by application within a DNA-encoded chemical library synthesis and subsequent discovery of enriched features from the library in selection experiments against two protein targets.

A hypodiboric acid system for the reduction of nitro groups on DNA-chemical conjugates has been developed. This transformation provided good to excellent yields of the reduced amine product for a variety of functionalized aromatic, heterocyclic, and aliphatic nitro compounds. DNA tolerance to reaction conditions, extension to decigram scale reductions, successful use in a DNA-encoded chemical library synthesis, and subsequent target selection are also described.

DNA-encoded chemical libraries (DELs) provide a high-throughput and cost-effective route for screening billions of unique molecules for binding affinity for diverse protein targets. Identifying candidate compounds from these libraries involves affinity selection, DNA sequencing, and measuring enrichment in a sample pool of DNA barcodes. Successful detection of potent binders is affected by many factors, including selection parameters, chemical yields, library amplification, sequencing depth, sequencing errors, library sizes, and the chosen enrichment metric. To date, there has not been a clear consensus about how enrichment from DEL selections should be measured or reported. We propose a normalized  z-score enrichment metric using a binomial distribution model that satisfies important criteria that are relevant for analysis of DEL selection data. The introduced metric is robust with respect to library diversity and sampling and allows for quantitative comparisons of enrichment of n-synthons from parallel DEL selections. These features enable a comparative enrichment analysis strategy that can provide valuable information about hit compounds in early stage drug discovery.

Accurate potential energy models are necessary for reliable atomistic simulations of chemical phenomena. In the realm of biomolecular modeling, large systems like proteins comprise very many noncovalent interactions (NCIs) that can contribute to the protein's stability and structure. This work presents two high-quality chemical databases of common fragment interactions in biomolecular systems as extracted from high-resolution Protein DataBank crystal structures: 3380 sidechain-sidechain interactions and 100 backbone-backbone interactions that inaugurate the BioFragment Database (BFDb). Absolute interaction energies are generated with a computationally tractable explicitly correlated coupled cluster with perturbative triples [CCSD(T)-F12] "silver standard" (0.05 kcal/mol average error) for NCI that demands only a fraction of the cost of the conventional "gold standard," CCSD(T) at the complete basis set limit. By sampling extensively from biological environments, BFDb spans the natural diversity of protein NCI motifs and orientations. In addition to supplying a thorough assessment for lower scaling force-field (2), semi-empirical (3), density functional (244), and wavefunction (45) methods (comprising >1M interaction energies), BFDb provides interactive tools for running and manipulating the resulting large datasets and offers a valuable resource for potential energy model development and validation.

Free energy perturbation theory, in combination with enhanced sampling of protein-ligand binding modes, is evaluated in the context of fragment-based drug design, and used to design two new small-molecule inhibitors of the Aurora A kinase-TPX2 protein-protein interaction.

We present a method to evaluate the free energies of ligand binding utilizing a Monte Carlo estimation of the configuration integrals concomitant with uncertainty quantification. Ensembles for integration are built through systematically perturbing an initial ligand conformation in a rigid binding pocket, which is optimized separately prior to incorporation of the ligand. We call the procedure producing the ensembles "blurring", and it is carried out using an in-house developed code. The Boltzmann factor contribution of each pose to the configuration integral is computed and from there the free energy is obtained. Potential function uncertainties are estimated using a fragment-based error propagation method. This method has been applied to a set of small aromatic ligands complexed with T4 Lysozyme L99A mutant. Microstate energies have been determined with the force fields ff99SB and ff94, and the semiempirical method PM6DH2 in conjunction with continuum solvation models including Generalized Born (GB), the Conductor-like Screening Model (COSMO), and SMD. Of the methods studied, PM6DH2-based scoring gave binding free energy estimates, which yielded a good correlation to the experimental binding affinities ( R 2 = 0.7). All methods overestimated the calculated binding affinities. We trace this to insufficient sampling, the single static protein structure, and inaccuracies in the solvent models we have used in this study.

Computer simulations are becoming an increasingly more important component of drug discovery. Computational models are now often able to reproduce and sometimes even predict outcomes of experiments. Still, potential energy models such as force fields contain significant amounts of bias and imprecision. We have shown how even small uncertainties in potential energy models can propagate to yield large errors, and have devised some general error-handling protocols for biomolecular modeling with imprecise energy functions. Herein we discuss those protocols within the contexts of protein-ligand binding and protein folding., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Computer-aided drug design could benefit from a greater understanding of how errors arise and propagate in biomolecular modeling. With such knowledge, model predictions could be associated with quantitative estimates of their uncertainty. In addition, novel algorithms could be designed to proactively reduce prediction errors. We investigated how errors propagate in statistical mechanical ensembles and found that free energy evaluations based on single molecular configurations yield maximum uncertainties in free energy. Furthermore, increasing the size of the ensemble by sampling and averaging over additional independent configurations reduces uncertainties in free energy dramatically. This finding suggests a general strategy that could be utilized as a post-hoc correction for improved precision in virtual screening and free energy estimation.

Computational models used in the estimation of thermodynamic quantities of large chemical systems often require approximate energy models that rely on parameterization and cancellation of errors to yield agreement with experimental measurements. In this work, we show how energy function errors propagate when computing statistical mechanics-derived thermodynamic quantities. Assuming that each microstate included in a statistical ensemble has a measurable amount of error in its calculated energy, we derive low-order expressions for the propagation of these errors in free energy, average energy, and entropy. Through gedanken experiments we show the expected behavior of these error propagation formulas on hypothetical energy surfaces. For very large microstate energy errors, these low-order formulas disagree with estimates from Monte Carlo simulations of error propagation. Hence, such simulations of error propagation may be required when using poor potential energy functions. Propagated systematic errors predicted by these methods can be removed from computed quantities, while propagated random errors yield uncertainty estimates. Importantly, we find that end-point free energy methods maximize random errors and that local sampling of potential energy wells decreases random error significantly. Hence, end-point methods should be avoided in energy computations and should be replaced by methods that incorporate local sampling. The techniques described herein will be used in future work involving the calculation of free energies of biomolecular processes, where error corrections are expected to yield improved agreement with experiment.

We describe a statistics-based model for the estimation of basis set superposition error (BSSE) for large biomolecular systems in which molecular fragment interactions are classified and analyzed with a linear model based on a bimolecular proximity descriptor. The models are trained independently for different classes of molecular interactions, quantum methods, and basis sets. The predicted fragment BSSE values, along with predicted uncertainties, are then propagated throughout the supermolecule to yield an overall estimate of BSSE and associated uncertainty. The method is described and demonstrated at the MP2/6-31G* and MP2/aug-cc-pVDZ levels of theory on a protein-ligand complex, a small helical protein, and a set of native and decoy folds of the Pin1 WW domain.

Two families of binding affinity estimation methodologies are described which were utilized in the SAMPL3 trypsin/fragment binding affinity challenge. The first is a free energy decomposition scheme based on a thermodynamic cycle, which included separate contributions from enthalpy and entropy of binding as well as a solvent contribution. Enthalpic contributions were estimated with PM6-DH2 semiempirical quantum mechanical interaction energies, which were modified with a statistical error correction procedure. Entropic contributions were estimated with the rigid-rotor harmonic approximation, and solvent contributions to the free energy were estimated with several different methods. The second general methodology is the empirical score LISA, which contains several physics-based terms trained with the large PDBBind database of protein/ligand complexes. Here we also introduce LISA+, an updated version of LISA which, prior to scoring, classifies systems into one of four classes based on a ligand's hydrophobicity and molecular weight. Each version of the two methodologies (a total of 11 methods) was trained against a compiled set of known trypsin binders available in the Protein Data Bank to yield scaling parameters for linear regression models. Both raw and scaled scores were submitted to SAMPL3. Variants of LISA showed relatively low absolute errors but also low correlation with experiment, while the free energy decomposition methods had modest success when scaling factors were included. Nonetheless, re-scaled LISA yielded the best predictions in the challenge in terms of RMS error, and six of these models placed in the top ten best predictions by RMS error. This work highlights some of the difficulties of predicting binding affinities of small molecular fragments to protein receptors as well as the benefit of using training data.

Basis set superposition error (BSSE) is a significant contributor to errors in quantum-based energy functions, especially for large chemical systems with many molecular contacts such as folded proteins and protein-ligand complexes. While the counterpoise method has become a standard procedure for correcting intermolecular BSSE, most current approaches to correcting intramolecular BSSE are simply fragment-based analogues of the counterpoise method which require many (two times the number of fragments) additional quantum calculations in their application. We propose that magnitudes of both forms of BSSE can be quickly estimated by dividing a system into interacting fragments, estimating each fragment's contribution to the overall BSSE with a simple statistical model, and then propagating these errors throughout the entire system. Such a method requires no additional quantum calculations, but rather only an analysis of the system's interacting fragments. The method is described herein and is applied to a protein-ligand system, a small helical protein, and a set of native and decoy protein folds., (© 2011 American Institute of Physics)

An energy expansion (binding energy decomposition into n-body interaction terms for n ≥ 2) to express the receptor-ligand binding energy for the fragmented HIV II protease-Indinavir system is described to address the role of cooperativity in ligand binding. The outcome of this energy expansion is compared to the total receptor-ligand binding energy at the Hartree-Fock, density functional theory, and semiempirical levels of theory. We find that the sum of the pairwise interaction energies approximates the total binding energy to ∼82% for HF and to >95% for both the M06-L density functional and PM6-DH2 semiempirical method. The contribution of the three-body interactions amounts to 18.7%, 3.8%, and 1.4% for HF, M06-L, and PM6-DH2, respectively. We find that the expansion can be safely truncated after n=3. That is, the contribution of the interactions involving more than three parties to the total binding energy of Indinavir to the HIV II protease receptor is negligible. Overall, we find that the two-body terms represent a good approximation to the total binding energy of the system, which points to pairwise additivity in the present case. This basic principle of pairwise additivity is utilized in fragment-based drug design approaches and our results support its continued use. The present results can also aid in the validation of non-bonded terms contained within common force fields and in the correction of systematic errors in physics-based score functions., (© 2011 American Institute of Physics)

The routine prediction of three-dimensional protein structure from sequence remains a challenge in computational biochemistry. It has been intuited that calculated energies from physics-based scoring functions are able to distinguish native from nonnative folds based on previous performance with small proteins and that conformational sampling is the fundamental bottleneck to successful folding. We demonstrate that as protein size increases, errors in the computed energies become a significant problem. We show, by using error probability density functions, that physics-based scores contain significant systematic and random errors relative to accurate reference energies. These errors propagate throughout an entire protein and distort its energy landscape to such an extent that modern scoring functions should have little chance of success in finding the free energy minima of large proteins. Nonetheless, by understanding errors in physics-based score functions, they can be reduced in a post-hoc manner, improving accuracy in energy computation and fold discrimination.

A largely unsolved problem in computational biochemistry is the accurate prediction of binding affinities of small ligands to protein receptors. We present a detailed analysis of the systematic and random errors present in computational methods through the use of error probability density functions, specifically for computed interaction energies between chemical fragments comprising a protein-ligand complex. An HIV-II protease crystal structure with a bound ligand (indinavir) was chosen as a model protein-ligand complex. The complex was decomposed into twenty-one (21) interacting fragment pairs, which were studied using a number of computational methods. The chemically accurate complete basis set coupled cluster theory (CCSD(T)/CBS) interaction energies were used as reference values to generate our error estimates. In our analysis we observed significant systematic and random errors in most methods, which was surprising especially for parameterized classical and semiempirical quantum mechanical calculations. After propagating these fragment-based error estimates over the entire protein-ligand complex, our total error estimates for many methods are large compared to the experimentally determined free energy of binding. Thus, we conclude that statistical error analysis is a necessary addition to any scoring function attempting to produce reliable binding affinity predictions.