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Preventing and controlling influenza virus infection remains a global public health challenge, as it causes seasonal epidemics to unexpected pandemics. These infections are responsible for high morbidity, mortality, and substantial economic impact.

Missense mutations in p53 are severely deleterious and occur in over 50% of all human cancers. The majority of these mutations are located in the inherently unstable DNA-binding domain (DBD), many of which destabilize the domain further and expose its aggregation-prone hydrophobic core, prompting self-assembly of mutant p53 into inactive cytosolic amyloid-like aggregates. Screening an oligopyridylamide library, previously shown to inhibit amyloid formation associated with Alzheimer’s disease and type II diabetes, identified a tripyridylamide, ADH-6, that abrogates self-assembly of the aggregation-nucleating subdomain of mutant p53 DBD. Moreover, ADH-6 targets and dissociates mutant p53 aggregates in human cancer cells, which restores p53’s transcriptional activity, leading to cell cycle arrest and apoptosis. Notably, ADH-6 treatment effectively shrinks xenografts harboring mutant p53, while exhibiting no toxicity to healthy tissue, thereby substantially prolonging survival. This study demonstrates the successful application of a bona fide small-molecule amyloid inhibitor as a potent anticancer agent., Amyloid aggregation of mutant p53 contributes to its loss of tumor suppressor function and oncogenic gain-of-function. Here, the authors use a protein mimetic to abrogate mutant p53 aggregation and rescue p53 function, which inhibits cancer cell proliferation in vitro and halts tumor growth in vivo.

Significant progress has been made in the field of a priori crystal structure prediction, with a number of recent remarkable success stories. Herein, we briefly outline the methods that have been d...

Order sets that adhere to disease-specific guidelines have been shown to increase clinician efficiency and patient safety but curating these order sets, particularly for consistency across multiple sites, is difficult and time consuming. We created software called CDS-Compare to alleviate the burden on expert reviewers in rapidly and effectively curating large databases of order sets. We applied our clustering-based software to a database of NLP-processed order sets extracted from VA's Electronic Health Record, then had subject-matter experts review the web application version of our software for clustering validity.

The worldwide outbreak of SARS-CoV-2 in early 2020 caused numer- ous deaths and unprecedented measures to control its spread. We employed our Computational Analysis of Novel Drug Opportunities (CANDO) multiscale therapeutic discovery, repurposing, and design platform to identify small molecule inhibitors of the virus to treat its resulting indication, COVID-19. Initially, few experimental studies existed on SARS-CoV-2, so we optimized our drug candidate prediction pipelines using results from two independent high-throughput screens against prevalent human coronaviruses. Ranked lists of candidate drugs were generated using our open source cando.py software based on viral protein inhibition and proteomic interaction similarity. For the former viral protein inhibition pipeline, we computed interaction scores between all compounds in the corresponding candidate library and eighteen SARS-CoV proteins using an interaction scoring protocol with extensive parameter optimization which was then applied to the SARS-CoV-2 proteome for prediction. For the latter similarity based pipeline, we computed interaction scores between all compounds and human protein structures in our libraries then used a consensus scoring approach to identify candidates with highly similar proteomic interaction signatures to multiple known anti-coronavirus actives. We published our ranked candidate lists at the very beginning of the COVID-19 pandemic. Since then, 51 of our 276 predictions have demonstrated anti-SARS-CoV-2 activity in published clinical and experimental studies. These results illustrate the ability our platform to rapidly respond to emergent pathogens and provide greater evidence that treating compounds in a multitarget context more accurately describes their behavior in biological systems.

1.AbstractPharmacogenomics is a rapidly growing field with the goal of providing personalized care to every patient. Previously, we developed the Computational Analysis of Novel Drug Opportunities (CANDO) platform for multiscale therapeutic discovery to screen optimal compounds for any indication/disease by performing analytics on their interactions with large protein libraries. We implemented a comprehensive precision medicine drug discovery pipeline within the CANDO platform to determine which drugs are most likely to be effective against mutant phenotypes of non-small cell lung cancer (NSCLC) based on the supposition that drugs with similar interaction profiles (or signatures) will have similar behavior and therefore show synergistic effects. CANDO predicted that osimertinib, an EGFR inhibitor, is most likely to synergize with four KRAS inhibitors.Validation studies with cellular toxicity assays confirmed that osimertinib in combination with ARS-1620, a KRAS G12C inhibitor, and BAY-293, a pan-KRAS inhibitor, showed a synergistic effect on decreasing cellular proliferation by acting on mutant KRAS. Gene expression studies revealed that MAPK suppression is a key correlate of decreased cellular proliferation following treatment with KRAS inhibitor BAY-293, but not treatment with ARS-1620 or osimertinib. Our precision medicine pipeline may be used to identify compounds capable of synergizing with inhibitors of KRAS G12C, and to assess their likelihood of becoming drugs by understanding their behavior at the proteomic/interactomic scales.

Humans are exposed to numerous compounds daily, some of which have adverse effects on health. Computational approaches for modeling toxicological data in conjunction with machine learning algorithms have gained popularity over the last few years. Machine learning methods have been used to predict toxicity-related biological activities using chemical structure descriptors. However, toxicity-related proteomic features have not been fully investigated. In this study, we construct a computational model using machine learning for selecting the most important proteins representing features in predicting the toxicity of the compounds in the Tox21 dataset using the multiscale Computational Analysis of Novel Drug Opportunities (CANDO) platform for therapeutic discovery. Tox21 is a highly imbalanced dataset consisting of twelve in vitro assays, seven from the nuclear receptor (NR) signaling pathway and five from the stress response (SR) pathway, for more than 10,000 compounds. For our computational model, we employed a random forest (RF) with the combination of Synthetic Minority Oversampling Technique (SMOTE) and Edited Nearest Neighbor (ENN) method, aka SMOTE+ENN, which is resampling method to balance the activity class distribution. Within the NR and SR pathways, the activity of the aryl hydrocarbon receptor (NR-AhR), toxicity mediating transcription factor, and mitochondrial membrane potential (SR-MMP) were two of the top-performing twelve toxicity endpoints with AUROCs of 0.90 and 0.92, respectively. The top extracted features for evaluating compound toxicity were passed into enrichment analysis to highlight the implicated biological pathways and proteins. We validated our enrichment results for the activity of the AhR using a thorough literature search. Our case study showed that the selected enriched pathways and proteins from our computational pipeline are not only correlated with NR-AhR toxicity but also form a cascading upstream/downstream arrangement. Our work elucidates significant relationships between protein and compound interactions computed using CANDO and the associated biological pathways to which the proteins belong, with twelve toxicity endpoints. This novel study uses machine learning not only to predict and understand toxicity but also elucidates therapeutic mechanisms at a proteomic level for a variety of toxicity endpoints.

Computational approaches have accelerated novel therapeutic discovery in recent decades. The Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multitarget therapeutic discovery, repurposing, and design aims to improve their efficacy and safety by employing a holistic approach that computes interaction signatures between every drug/compound and a large library of non-redundant protein structures corresponding to the human proteome fold space. These signatures are compared and analyzed to determine if a given drug/compound is efficacious and safe for a given indication/disease. In this study, we used a deep learning-based autoencoder to first reduce the dimensionality of CANDO-computed drug–proteome interaction signatures. We then employed a reduced conditional variational autoencoder to generate novel drug-like compounds when given a target encoded “objective” signature. Using this approach, we designed compounds to recreate the interaction signatures for twenty approved and experimental drugs and showed that 16/20 designed compounds were predicted to be significantly (p-value ≤ 0.05) more behaviorally similar relative to all corresponding controls, and 20/20 were predicted to be more behaviorally similar relative to a random control. We further observed that redesigns of objectives developed via rational drug design performed significantly better than those derived from natural sources (p-value ≤ 0.05), suggesting that the model learned an abstraction of rational drug design. We also show that the designed compounds are structurally diverse and synthetically feasible when compared to their respective objective drugs despite consistently high predicted behavioral similarity. Finally, we generated new designs that enhanced thirteen drugs/compounds associated with non-small cell lung cancer and anti-aging properties using their predicted proteomic interaction signatures. his study represents a significant step forward in automating holistic therapeutic design with machine learning, enabling the rapid generation of novel, effective, and safe drug leads for any indication.

Dysregulated pre-mRNA splicing is an emerging Achilles heel of cancers and myelodysplasias. To expand the currently limited portfolio of small molecule drug leads, we screened for chemical modulators of the U2AF complex, which nucleates spliceosome assembly and is mutated in myelodysplasias. A hit compound specifically enhances RNA binding by a U2AF2 subunit. Remarkably, the compound inhibits splicing of representative substrates in cells and stalls spliceosome assembly at the stage of U2AF function. Computational docking, together with structure-guided mutagenesis, indicates that the compound bridges an active conformation of the U2AF2 RNA recognition motifs via hydrophobic and electrostatic moieties. Altogether, our results highlight the potential of trapping early spliceosome assembly as an effective pharmacological means to manipulate pre-mRNA splicing. By extension, we suggest that stabilizing inactive checkpoints may offer a breakthrough approach for small molecule inhibition of multi-stage macromolecular assemblies.

In this manuscript we highlight consensus between the list of drugs currently in clinical trials to treat COVID-19, the worldwide pandemic caused by severe acute respiratory coronavirus 2 (SARS-CoV-2), and the list of predictions made using our shotgun drug discovery, repurposing, and design platform known as CANDO (Computational Analysis of Novel Drug Opportunities). We make the argument that increased funding and development for drug repurposing biotechnology like ours will help combat the inevitable pathogenic outbreaks of the future.

Drug-repurposing technologies are growing in number and maturing. However, comparisons to each other and to reality are hindered because of a lack of consensus with respect to performance evaluation. Such comparability is necessary to determine scientific merit and to ensure that only meaningful predictions from repurposing technologies carry through to further validation and eventual patient use. Here, we review and compare performance evaluation measures for these technologies using version 2 of our shotgun repurposing Computational Analysis of Novel Drug Opportunities (CANDO) platform to illustrate their benefits, drawbacks, and limitations. Understanding and using different performance evaluation metrics ensures robust cross-platform comparability, enabling us to continue to strive toward optimal repurposing by decreasing the time and cost of drug discovery and development.

Drug repurposing technologies are growing in number and maturing. However, comparison to each other and to reality is hindered due to lack of consensus with respect to performance evaluation. Such comparability is necessary to determine scientific merit and to ensure that only meaningful predictions from repurposing technologies carry through to further validation and eventual patient use. Here, we review and compare performance evaluation measures for these technologies using version 2 of our shotgun repurposing Computational Analysis of Novel Drug Opportunities (CANDO) platform to illustrate their benefits, drawbacks, and limitations. Understanding and using different performance evaluation metrics ensures robust cross platform comparability, enabling us to continuously strive towards optimal repurposing by decreasing time and cost of drug discovery and development.

Missense mutations in p53 are severely deleterious and occur in over 50% of all human cancers. The vast majority of these mutations are located in the inherently unstable DNA-binding domain (DBD), many of which destabilize the domain further and expose its aggregation-prone hydrophobic core, prompting self-assembly of mutant p53 into inactive cytosolic amyloid-like aggregates. Screening an oligopyridylamide library, previously shown to inhibit amyloid formation associated with Alzheimer’s disease and type II diabetes, identified a tripyridylamide, ADH-6, that potently abrogates self-assembly of the aggregation-nucleating subdomain of mutant p53 DBD. Moreover, ADH-6 effectively targets and dissociates mutant p53 aggregates in human cancer cells, which restores p53’s transcriptional activity, leading to cell cycle arrest and apoptosis. Notably, ADH-6 treatment substantially shrinks xenografts harboring mutant p53 and prolongs survival, while exhibiting no toxicity to healthy tissue. This study demonstrates the first successful application of a bona fide small-molecule amyloid inhibitor as an anticancer agent.

Traditional drug discovery methods focus on optimizing the efficacy of a drug against a single biological target of interest for a specific disease. However, evidence supports the multitarget theory, i.e., drugs work by exerting their therapeutic effects via interaction with multiple biological targets, which have multiple phenotypic effects. Analytics of drug-protein interactions on a large proteomic scale provides insight into disease systems while also allowing for prediction of putative therapeutics against specific indications. We present a Python package for analysis of drug-proteome and drug-disease relationships implementing the Computational Analysis of Novel Drug Opportunities (CANDO) platform. The CANDO package allows for rapid drug similarity assessment, most notably via an in-house interaction scoring protocol where billions of drug-protein interactions are rapidly scored and the similarity of drug-proteome interaction signatures is calculated. The package also implements a variety of benchmarking protocols for shotgun drug discovery and repurposing, i.e., to determine how every known drug is related to every other in the context of the indications/diseases for which they are approved. Drug predictions are generated through consensus scoring of the most similar compounds to drugs known to treat a particular indication. Support for comparing and ranking novel chemical entities, as well as machine learning modules for both benchmarking and putative drug candidate prediction is also available. The CANDO Python package is available on GitHub at https://github.com/ram-compbio/CANDO, through the Conda Python package installer, and at http://compbio.org/software/.

The likelihood of continued circulation of COVID-19 and its variants, and novel coronaviruses due to future zoonotic transmissions, combined with the current paucity of coronavirus antivirals, emphasize the need for improved screening in developing effective antivirals for the treatment of infection by SARS-CoV-2 (CoV2) and other coronaviruses. Here we report the development of a live-cell based assay for evaluating the intracellular function of the critical, highly-conserved CoV2 target, the Main 3C-like protease (Mpro). This assay is based on expression of native wild-type mature CoV2 Mpro, the function of which is quantitatively evaluated in living cells through cleavage of a biosensor leading to loss of fluorescence. Evaluation does not require cell harvesting, allowing for multiple measurements from the same cells facilitating quantification of Mpro inhibition, as well as recovery of function upon removal of inhibitory drugs. The pan-coronavirus Mpro inhibitor, GC376, was utilized in this assay and effective inhibition of intracellular CoV2 Mpro was found to be consistent with levels required to inhibit CoV2 infection of human lung cells. We demonstrate that GC376 is an effective inhibitor of intracellular CoV2 Mpro at low micromolar levels, while other predicted Mpro inhibitors, bepridil and alverine, are not. Results indicate this system can provide a highly effective high-throughput coronavirus Mpro screening system.

Elucidating drug-protein interactions is essential for understanding the beneficial effects of small molecule therapeutics in human disease states. Traditional drug discovery methods focus on optimizing the efficacy of a drug against a single biological target of interest. However, evidence supports the multitarget theory, i.e., drugs work by exerting their therapeutic effects via interaction with multiple biological targets. Analyzing drug interactions with a library of proteins provides further insight into disease systems while also allowing for prediction of putative therapeutics against specific indications. We present a Python package for analysis of drug-proteome and drug-disease relationships implementing the Computational Analysis of Novel Drug Opportunities (CANDO) platform [1–7]. The CANDO package allows for rapid drug similarity assessment, most notably via the bioinformatic docking protocol where billions of drug-protein interactions are rapidly scored and the similarity of drug-proteome interaction signatures is calculated. The package also implements a variety of bench-marking protocols to determine how well drugs are related to each other in terms of the indications/diseases for which they are approved. Drug predictions are generated through consensus scoring of the most similar compounds to drugs known to treat a particular indication. Support for comparing and ranking novel chemical entities, as well as machine learning modules for both benchmarking and putative drug candidate prediction is also available. The CANDO Python package is available on GitHub at https://github.com/ram-compbio/CANDO, through the Conda Python package installer, and at http://compbio.org/software/.

BackgroundDrug discovery is an arduous process that requires many years and billions of dollars before approval for patient use. However, there are a number of drugs and human ingestibles approved for a variety of indications/diseases that can be potentially repurposed as new treatments for others, decreasing the time and cost required.MethodsCANDO (Computational Analysis of Novel Drug Opportunities) is a platform for shotgun, multitarget drug discovery and repurposing. The CANDO platform scores interactions between 46,784 proteins structures and 3,733 human use compounds using a bioinformatic docking protocol to generate compound-proteome interaction signatures that are then compared to identify candidates for repurposing. Benchmarking of the platform is accomplished by comparing the compound-proteome interaction signatures and determining whether signatures corresponding to pairs of drugs approved for the same indication fall within particular cutoffs.ResultsWe have altered the scoring function of bioinformatic docking protocol in the newest version of our platform (v1.5) to use the best OBscore for each compound-protein interaction, resulting in an increased benchmarking accuracy from 11.7% in v1 to 12.8% in v1.5 for the top10 cutoff, the most stringent one used, and correspondingly from 24.9% to 31.2% for the top100 cutoff.ConclusionsThe change in the interaction scoring and other bug fixes in CANDO v1.5 have resulted in improved benchmarking performance, making the platform more effective at predicting novel, therapeutic drug-indication pairs.

Species likely to be present in methylaluminoxane (MAO) are studied via dispersion-corrected DFT, which we show is able to accurately predict thermochemical parameters for the dimerization of trimethylaluminum (TMA). Both cage-like, (AlOMe)n,c, and TMA-bound nanotubes, (AlOMe)n,t·(AlMe3)m, are found to be important components of MAO. The most stable structures have aluminum/oxygen atoms in environments whose average hybridization approaches sp3/sp2. The (AlOMe)n,t·(AlMe3)m isomers with the lowest free energies possess Al−μ-Me–Al bonds. At 298 K a novel Td-(AlOMe)16,c oligomer is one of the most stable structures among the six stoichiometries with the lowest free energies: (AlOMe)20,c·(AlMe3)2, Td-(AlOMe)16,c, (AlOMe)18,c, (AlOMe)20,c·(AlMe3), (AlOMe)10,t·(AlMe3)4, and (AlOMe)20,c. As the temperature rises, the abundance of (AlOMe)n,t·(AlMe3)m decreases, and that of (AlOMe)n,c increases. Because the former are expected to be precursors for the active species in polymerization, this may in part be the reaso...

Calculations of NMR shielding tensors and nuclear quadrupole coupling (NQC) tensors at the Kohn-Sham density functional level are used to simulate (27)Al magic-angle spinning (MAS) NMR spectra of the important olefin polymerization co-catalyst methylaluminoxane (MAO) at 77, 298, 398, and 498 K and spectrometer magnetic field inductions B ranging from 14.1 to 23.5 T. The calculations utilize the temperature (T) dependent distribution of species present in MAO determined recently by Zurek and coworkers from first-principles theory [Macromolecules, 2014, 47, 8556]. The NMR calculations suggest that variable-T and variable-B NMR measurements are able to quantify the ratio of free versus bound trimethyl-aluminum (TMA) in MAO via characteristic spectral features assigned to 3-coordinate and 4-coordinate Al sites in MAO as well as spectral features arising from free TMA or its dimer. The T-dependent distribution of species causes other characteristic features in the NMR spectra to appear/disappear that can be associated with different aluminum environments such as square vs. hexagonal faces in cage and tubular structures. The simulated spectra at 298 K and 19.6 T are in reasonably good agreement with the experimental solid-state NMR (SSNMR) spectra obtained previously for MAO gel. The promise and limitations of solid-state NMR to unravel the enigma surrounding the structure(s) of MAO are discussed.

Evolutionary algorithms (EAs) coupled with density functional theory (DFT) calculations have been used to predict the most stable hydrides of phosphorus (PHn, n = 1-6) at 100, 150, and 200 GPa. At these pressures phosphine is unstable with respect to decomposition into the elemental phases, as well as PH2 and H2. Three metallic PH2 phases were found to be dynamically stable and superconducting between 100 and 200 GPa. One of these contains five formula units in the primitive cell and has C2/m symmetry (5FU-C2/m). It comprises 1D periodic PH3-PH-PH2-PH-PH3 oligomers. Two structurally related phases consisting of phosphorus atoms that are octahedrally coordinated by four phosphorus atoms in the equatorial positions and two hydrogen atoms in the axial positions (I4/mmm and 2FU-C2/m) were the most stable phases between ∼160-200 GPa. Their superconducting critical temperatures (Tc) were computed as 70 and 76 K, respectively, via the Allen-Dynes modified McMillan formula and using a value of 0.1 for the Coulomb pseudopotential, μ*. Our results suggest that the superconductivity recently observed by Drozdov, Eremets, and Troyan when phosphine was subject to pressures of 207 GPa in a diamond anvil cell may result from these, and other, decomposition products of phosphine.

A new version of XtalOpt , a user-friendly GPL-licensed evolutionary algorithm for crystal structure prediction, is available for download from the CPC library or the XtalOpt website, http://xtalopt.openmolecules.net . The new version now supports four external geometry optimization codes ( VASP , GULP , PWSCF , and CASTEP ), as well as three queuing systems: PBS, SGE, SLURM, and “Local”. The local queuing system allows the geometry optimizations to be performed on the userʼs workstation if an external computational cluster is unavailable. Support for the Windows operating system has been added, and a Windows installer is provided. Numerous bugfixes and feature enhancements have been made in the new release as well. New version program summary Program title: XtalOpt Catalogue identifier: AEGX_v2_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEGX_v2_0.html Program obtainable from: CPC Program Library, Queenʼs University, Belfast, N. Ireland Licensing provisions: GPL v2.1 or later [1] No. of lines in distributed program, including test data, etc.: 125 383 No. of bytes in distributed program, including test data, etc.: 11 607 415 Distribution format: tar.gz Programming language: C++ Computer: PCs, workstations, or clusters Operating system: Linux, MS Windows Classification: 7.7 External routines: Qt [2], Open Babel [3], Avogadro [4], and one of: VASP [5], PWSCF [6], GULP [7], CASTEP [8] Catalogue identifier of previous version: AEGX_v1_0 Journal reference of previous version: Comput. Phys. Comm. 182 (2011) 372 Does the new version supersede the previous version?: Yes Nature of problem: Predicting the crystal structure of a system from its stoichiometry alone remains a grand challenge in computational materials science, chemistry, and physics. Solution method: Evolutionary algorithms are stochastic search techniques which use concepts from biological evolution in order to locate the global minimum of a crystalline structure on its potential energy surface. Our evolutionary algorithm, XtalOpt , is freely available for use and collaboration under the GNU Public License. See the original publication on XtalOpt ʼs implementation [11] for more information on the method. Reasons for new version: Since XtalOpt ʼs initial release in June 2010, support for additional optimizers, queuing systems, and an operating system has been added. XtalOpt can now use VASP, GULP, PWSCF, or CASTEP to perform local geometry optimizations. The queue submission code has been rewritten, and now supports running any of the above codes on ssh-accessible computer clusters that use the Portable Batch System (PBS), Sun Grid Engine (SGE), or SLURM queuing systems for managing the optimization jobs. Alternatively, geometry optimizations may be performed on the userʼs workstation using the new internal “Local” queuing system if high performance computing resources are unavailable. XtalOpt has been built and tested on the Microsoft Windows operating system (XP or later) in addition to Linux, and a Windows installer is provided. The installer includes a development version of Avogadro that contains expanded crystallography support [12] that is not available in the mainline Avogadro releases. Other notable new developments include: • LIBSSH [10] is distributed with the XtalOpt sources and used for communication with the remote clusters, eliminating the previous requirement to set up public-key authentication; • Plotting enthalpy (or energy) vs. structure number in the plot tab will trace out the history of the most stable structure as the search progresses A read-only mode has been added to allow inspection of previous searches through the user interface without connecting to a cluster or submitting new jobs; • The tutorial [13] has been rewritten to reflect the changes to the interface and the newly supported codes. Expanded sections on optimizations schemes and save/resume have been added; • The included version of SPGLIB has been updated. An option has been added to set the Cartesian tolerance of the space group detection. A new option has been added to the Progress tableʼs right-click menu that copies the selected structureʼs POSCAR formatted representation to the clipboard; • Numerous other small bugfixes/enhancements. Summary of revisions: See “Reasons for new version” above. Running time: User dependent. The program runs until stopped by the user. References: [1] http://www.gnu.org/licenses/gpl.html . [2] http://www.trolltech.com/ . [3] http://openbabel.org/ . [4] http://avogadro.openmolecules.net . [5] http://cms.mpi.univie.ac.at/vasp . [6] http://www.quantum-espresso.org . [7] https://www.ivec.org/gulp . [8] http://www.castep.org . [9] http://spglib.sourceforge.net . [10] http://www.libssh.org . [11] D. Lonie, E. Zurek, Comp. Phys. Comm. 182 (2011) 372–387, doi:10.1016/j.cpc.2010.07.048 . [12] http://davidlonie.blogspot.com/2011/03/new-avogadro-crystallography-extension.html . [13] http://xtalopt.openmolecules.net/globalsearch/docs/tut-xo.html .

An automated stochastic docking program with a graphical user interface, RANDOMDOCK (RD), has been developed to aid the development of molecularly imprinted polymers and xerogels. RD supports computations with ab initio and semiempirical quantum chemistry programs. The RD algorithms have been tested by searching for the most stable geometries of a varying number of methacrylic acid molecules interacting with nicotinamide. The optimal structures found are either as stable or more stable than those previously proposed for this molecularly imprinted polymer, illustrating that RD is capable of identifying the lowest-energy structures out of a potentially vast number of possible configurations. RD was subsequently applied to determine the most favorable binding sites between silane molecules and tetracycline (TC) as well as TC analogues. Hydrogen bonding between the templates and a silane is an important determinant of stability. Dispersion interactions are also sizable, sometimes dominant, especially between the largest silane and TC analogues not possessing a site readily available for hydrogen bonding. We highlight the importance of exploring the full intermolecular potential energy landscape when studying systems which may not afford highly specific interactions.