Short Molecular Dynamics Research Articles

Binary-QSAR guided virtual screening of FDA approved drugs and compounds in clinical investigation against SARS-CoV-2 main protease.

With the emergence of the new SARS-CoV-2 virus, drug repurposing studies have gained substantial importance. Combined with the efficacy of recent improvements in ligand- and target-based virtual screening approaches, virtual screening has become faster and more productive than ever. In the current study, an FDA library of approved drugs and compounds under clinical investigation were screened for their antiviral activity using the antiviral therapeutic activity binary QSAR model of the MetaCore/MetaDrug platform. Among 6733-compound collection, we found 370 compounds with a normalized therapeutic activity value greater than a cutoff of 0.75. Only these selected compounds were used for molecular docking studies against the SARS-CoV-2 main protease (Mpro). After initial short (10 ns) molecular dynamics (MD) simulations with the top-50 docking scored compounds and following molecular mechanics generalized born surface area (MM/GBSA) calculations, top-10 compounds were subjected to longer (100 ns) MD simulations and end-point MM/GBSA estimations. Our virtual screening protocol yielded Cefuroxime pivoxetil, an ester prodrug of second-generation cephalosporin antibiotic Cefuroxime, as being a considerable molecule for drug repurposing against the SARS-CoV-2 Mpro.

New Features of the Open Source Monte Carlo Software Brick-CFCMC: Thermodynamic Integration and Hybrid Trial Moves.

We present several new major features added to the Monte Carlo (MC) simulation code Brick-CFCMC for phase- and reaction equilibria calculations (https://gitlab.com/ETh_TU_Delft/Brick-CFCMC). The first one is thermodynamic integration for the computation of excess chemical potentials (μex). For this purpose, we implemented the computation of the ensemble average of the derivative of the potential energy with respect to the scaling factor for intermolecular interactions (). Efficient bookkeeping is implemented so that the quantity is updated after every MC trial move with negligible computational cost. We demonstrate the accuracy and reliability of the calculation of μex for sodium chloride in water. Second, we implemented hybrid MC/MD translation and rotation trial moves to increase the efficiency of sampling of the configuration space. In these trial moves, short Molecular Dynamics (MD) trajectories are performed to collectively displace or rotate all molecules in the system. These trajectories are accepted or rejected based on the total energy drift. The efficiency of these trial moves can be tuned by changing the time step and the trajectory length. The new trial moves are demonstrated using MC simulations of a viscous fluid (deep eutectic solvent).

Invariance principles in the theory and computation of transport coefficients

In this work, we elaborate on two recently discovered invariance principles, according to which transport coefficients are, to a large extent, independent of the microscopic definition of the densities and currents of the conserved quantities being transported (energy, momentum, mass, charge). The first such principle, gauge invariance, allows one to define a quantum adiabatic energy current from density-functional theory, from which the heat conductivity can be uniquely defined and computed using equilibrium ab initio molecular dynamics. When combined with a novel topological definition of atomic oxidation states, gauge invariance also sheds new light onto the mechanisms of charge transport in ionic conductors. The second principle, convective invariance, allows one to extend the analysis to multi-component systems. These invariance principles can be combined with new spectral analysis methods for the current time series to be fed into the Green–Kubo formula to obtain accurate estimates of transport coefficients from relatively short molecular dynamics simulations.Graphic abstract

ClustENMD: efficient sampling of biomolecular conformational space at atomic resolution.

SummaryEfficient sampling of conformational space is essential for elucidating functional/allosteric mechanisms of proteins and generating ensembles of conformers for docking applications. However, unbiased sampling is still a challenge especially for highly flexible and/or large systems. To address this challenge, we describe a new implementation of our computationally efficient algorithm ClustENMD that is integrated with ProDy and OpenMM softwares. This hybrid method performs iterative cycles of conformer generation using elastic network model for deformations along global modes, followed by clustering and short molecular dynamics simulations. ProDy framework enables full automation and analysis of generated conformers and visualization of their distributions in the essential subspace.Availability and implementationClustENMD is open-source and freely available under MIT License from https://github.com/prody/ProDy.Supplementary information Supplementary data are available at Bioinformatics online.

Transcription factor NF-κB as target for SARS-CoV-2 drug discovery efforts using inflammation-based QSAR screening model

NF-κB is a central regulator of immunity and inflammation. It is suggested that the inflammatory response mediated by SARS-CoV-2 is predominated by NF-κB activation. Thus, NF-κB inhibition is considered a potential therapeutic strategy for COVID-19. The aim of this study was to identify potential anti-inflammation lead molecules that target NF-κB using a quantitative structure-activity relationships (QSAR) model of currently used and investigated anti-inflammatory drugs as the basis for screening. We applied an integrated approach by starting with the inflammation-based QSAR model to screen three libraries containing more than 220,000 drug-like molecules for the purpose of finding potential drugs that target the NF-κB/IκBα p50/p65 (RelA) complex. We also used QSAR models to rule out molecules that were predicted to be toxic. Among screening libraries, 382 molecules were selected as potentially nontoxic and were analyzed further by short and long molecular dynamics (MD) simulations and free energy calculations. We have discovered five hit ligands with highly predicted anti-inflammation activity and nearly no predicted toxicities which had strongly favorable protein-ligand interactions and conformational stability at the binding pocket compared to a known NF-κB inhibitor (procyanidin B2). We propose these hit molecules as potential NF-κB inhibitors which can be further investigated in pre-clinical studies against SARS-CoV-2 and may be used as a scaffold for chemical optimization and drug development efforts.

Molecular Dynamics Scoring of Protein-Peptide Models Derived from Coarse-Grained Docking.

One of the major challenges in the computational prediction of protein–peptide complexes is the scoring of predicted models. Usually, it is very difficult to find the most accurate solutions out of the vast number of sometimes very different and potentially plausible predictions. In this work, we tested the protocol for Molecular Dynamics (MD)-based scoring of protein–peptide complex models obtained from coarse-grained (CG) docking simulations. In the first step of the scoring procedure, all models generated by CABS-dock were reconstructed starting from their original C-alpha trace representations to all-atom (AA) structures. The second step included geometry optimization of the reconstructed complexes followed by model scoring based on receptor–ligand interaction energy estimated from short MD simulations in explicit water. We used two well-known AA MD force fields, CHARMM and AMBER, and a CG MARTINI force field. Scoring results for 66 different protein–peptide complexes show that the proposed MD-based scoring approach can be used to identify protein–peptide models of high accuracy. The results also indicate that the scoring accuracy may be significantly affected by the quality of the reconstructed protein receptor structures.

Focus on the deformation mechanism at the interfacial layer in nano-reinforced polymers: A molecular dynamics study of silica - poly(methyl methacrylate) nano-composite

The effects of nanoparticle size on the “macroscopic” mechanical response and interfacial interaction in the case of model nano-reinforced polymers were investigated using molecular dynamics simulations. Different ensembles, of homogeneous polymer matrices, amorphous silica particle, and their binary mixtures were prepared. The binary mixture was made with silica nano-particle 3 nm in size, embedded in poly (methyl methacrylate) or PMMA polymeric matrix. At the macroscopic scale, the mechanical response of the matrix and nano-composite was evaluated using simulated tensile tests. Interfacial interaction between the NP and the PMMA matrix was qualitatively evaluated using the thermodynamic analysis of nanocomposite systems. Entropy (S) and internal energy (E) were derived from relatively short molecular dynamics trajectories, using the two-phase thermodynamic method (2-PT). The PMMA matrix was decomposed into concentric layers composed of atoms from different polymer chains but located at an equal distance from the center of mass of the silica NP. For both nanocomposite systems, the interface layer of the polymer closest to the silica NP surface exhibited both the lowest entropy and a well-organized structure. Entropy and internal energy patterns were derived from tensile stretched samples. Entropy and internal energy variation on stretched samples revealed the existence of two distinct domains. The first domain deformation was a mixture of internal energy increase and entropy decrease. In the second domain, the deformation mechanism was mostly governed by variations in entropy. These observations will be discussed about polymer – nanoparticle attractivity.

Open Catalyst 2020 (OC20) Dataset and Community Challenges

Catalyst discovery and optimization is key to solving many societal and energy challenges including solar fuel synthesis, long-term energy storage, and renewable fertilizer production. Despite considerable effort by the catalysis community to apply machine learning models to the computational catalyst discovery process, it remains an open challenge to build models that can generalize across both elemental compositions of surfaces and adsorbate identity/configurations, perhaps because datasets have been smaller in catalysis than in related fields. To address this, we developed the OC20 dataset, consisting of 1,281,040 density functional theory (DFT) relaxations (∼264,890,000 single-point evaluations) across a wide swath of materials, surfaces, and adsorbates (nitrogen, carbon, and oxygen chemistries). We supplemented this dataset with randomly perturbed structures, short timescale molecular dynamics, and electronic structure analyses. The dataset comprises three central tasks indicative of day-to-day catalyst modeling and comes with predefined train/validation/test splits to facilitate direct comparisons with future model development efforts. We applied three state-of-the-art graph neural network models (CGCNN, SchNet, and DimeNet++) to each of these tasks as baseline demonstrations for the community to build on. In almost every task, no upper limit on model size was identified, suggesting that even larger models are likely to improve on initial results. The dataset and baseline models are both provided as open resources as well as a public leader board to encourage community contributions to solve these important tasks.

Binding Selectivity of Inhibitors toward Bromodomains BAZ2A and BAZ2B Uncovered by Multiple Short Molecular Dynamics Simulations and MM-GBSA Calculations.

Two Bromodomain-Containing proteins BAZ2A and BAZ2B are responsible for remodeling chromatin and regulating noncoding RNAs. As for our current studies, integration of multiple short molecular dynamics simulations (MSMDSs) with molecular mechanics generalized Born surface area (MM-GBSA) method is adopted for insights into binding selectivity of three small molecules D8Q, D9T and UO1 to BAZ2A against BAZ2B. The calculations of MM-GBSA unveil that selectivity of inhibitors toward BAZ2A and BAZ2B highly depends on the enthalpy changes and the details uncover that D8Q has better selectivity toward BAZ2A than BAZ2B, D9T more favorably bind to BAZ2B than BAZ2A, and UO1 does not show obvious selectivity toward these two proteins. The analysis of interaction network between residues and inhibitors indicates that seven residues are mainly responsible for the selectivity of D8Q, six residues for D9T and four residues provide significant contributions to associations of UO1 with two proteins. Moreover the analysis of interaction network not only reveals warm spots of inhibitor bindings to BAZ2A and BAZ2B but also unveils that common residue pairs, including (W1816, W1887), (P1817, P1888), (F1818, F1889), (V1822, V1893), (N1823, N1894),(L1826, L1897), (V1827, V1898), (F1872, F1943), (N1873, N1944) and (V1879, I1950) belonging to (BAZ2A, BAZ2B), induce mainly binding differences of inhibitors to BAZ2A and BAZ2B. Hence, insights from our current studies offer useful dynamics information relating with conformational alterations and structure-affinity relationship at atomistic levels for novel therapeutic strategies toward BAZ2A and BAZ2B.

Hybrid In Silico and TR-FRET-Guided Discovery of Novel BCL-2 Inhibitors.

B-Cell lymphoma 2 (BCL-2) regulates cell death in humans. In this study, combined multiscale in silico approaches and in vitro studies were employed. A small-molecule library that includes more than 210 000 compounds was used. The predicted therapeutic activity value (TAV) of the compounds in this library was computed with the binary cancer quantitative structure-activity relationships (QSAR) model. The molecules with a high calculated TAV were used in 26 individual toxicity QSAR models. As a result of this screening protocol, 288 nontoxic molecules with high predicted TAV were identified. These selected hits were then screened against the BCL-2 target protein using hybrid docking and molecular dynamics (MD) simulations. The interaction energies of identified compounds were compared with two known BCL-2 inhibitors. Then, the short MD simulations were carried out by initiating the best docking poses of 288 molecules. Average MM/GBSA energies were computed, and long MD simulations were employed to selected hits. The same calculations were also applied for two known BCL-2 inhibitors. Moreover, a five-site (AHRRR) structure-based pharmacophore model was constructed, and this model was used in the screening of the same database. On the basis of hybrid data-driven ligand identification study, final hits were selected and used in in vitro studies. Based on results of the time-resolved fluorescence resonance energy transfer (TR-FRET) analysis, further filtration was carried out for the U87-MG cell line tests. MTT cell proliferation assay analysis results showed that selected three potent compounds were significantly effective on glioma cells.

Machine learning of free energies in chemical compound space using ensemble representations: Reaching experimental uncertainty for solvation.

Free energies govern the behavior of soft and liquid matter, and improving their predictions could have a large impact on the development of drugs, electrolytes, or homogeneous catalysts. Unfortunately, it is challenging to devise an accurate description of effects governing solvation such as hydrogen-bonding, van der Waals interactions, or conformational sampling. We present a Free energy Machine Learning (FML) model applicable throughout chemical compound space and based on a representation that employs Boltzmann averages to account for an approximated sampling of configurational space. Using the FreeSolv database, FML's out-of-sample prediction errors of experimental hydration free energies decay systematically with training set size, and experimental uncertainty (0.6 kcal/mol) is reached after training on 490 molecules (80% of FreeSolv). Corresponding FML model errors are on par with state-of-the art physics based approaches. To generate the input representation for a new query compound, FML requires approximate and short molecular dynamics runs. We showcase its usefulness through analysis of solvation free energies for 116k organic molecules (all force-field compatible molecules in the QM9 database), identifying the most and least solvated systems and rediscovering quasi-linear structure-property relationships in terms of simple descriptors such as hydrogen-bond donors, number of NH or OH groups, number of oxygen atoms in hydrocarbons, and number of heavy atoms. FML's accuracy is maximal when the temperature used for the molecular dynamics simulation to generate averaged input representation samples in training is the same as for the query compounds. The sampling time for the representation converges rapidly with respect to the prediction error.

Structure-based identification of inhibitors disrupting the CD2-CD58 interactions.

The immune system has very intricate mechanisms of fighting against the invading infections which are accomplished by a sequential event of molecular interactions in the body. One of the crucial phenomena in this process is the recognition of T-cells by the antigen-presenting cells (APCs), which is initiated by the rapid interaction between both cell surface receptors, i.e., CD2 located on T-cells and CD58 located on APCs. Under various pathological conditions, which involve undesired immune response, inhibiting the CD2-CD58 interactions becomes a therapeutically relevant opportunity. Herein we present an extensive work to identify novel inhibiting agents of the CD2-CD58 interactions. Classical molecular dynamics (MD) simulations of the CD2-CD58 complex highlighted a series of crucial CD58 residues responsible for the interactions with CD2. Based on such results, a pharmacophore map, complementary to the CD2-binding site of CD58, was created and employed for virtual screening of ~ 300,000 available compounds. On the ~ 6000 compounds filtered from pharmacophore mapping, ADME screening leads to ~ 350 molecules. Molecular docking was then performed on these molecules, and fifteen compounds emerged with significant binding energy (< - 50kcal/mol) for CD58. Finally, short MD simulations were performed in triplicate on each complex (i) to provide a microscopic view of the ligand binding and (ii) to rule out possibly weak binders of CD58 from the identified hits. At last, we suggest eight compounds for in vitro testing that were identified as promising hits to bind CD58 with a high binding affinity.

Kinetics and Free Energy of Protein Ligand Interaction using Weighted Ensemble Milestoning (WEM)

Ligand-receptor interactions are pivotal to the functioning of biological processes inside cells. Molecular dynamics (MD) simulations can provide mechanistic insight of ligand binding and unbinding processes with atomistic detail. But most protein-ligand interactions are characterized as rare events for which the timescales can often go up to milliseconds to hours. They are difficult or sometimes impossible to probe using direct MD simulations with the present day computational facilities. We developed the weighted ensemble milestoning (WEM) scheme which combines two established path sampling techniques, weighted ensemble (WE) and milestoning, to efficiently sample biophysical rare events and calculate the kinetics and the free energy profile from combining multiple short and low cost MD trajectories.

Protonation Dynamics in the K-Channel of Cytochrome c Oxidase Estimated from Molecular Dynamics Simulations

Proton transfer reactions are one of the most fundamental processes in biochemistry. We present a simplistic approach for estimating proton transfer probabilities in a membrane protein, cytochrome c oxidase. We combine short molecular dynamics simulations at discrete protonation states with a Monte Carlo approach to exchange between those states. Requesting for a proton transfer the existence of a hydrogen-bonded connection between the two source and target residues of the exchange, restricts the acceptance of transfers to only those in which a proton-relay is possible. Together with an analysis of the hydrogen-bonded connectivity in one of the proton-conducting channels of cytochrome c oxidase, this approach gives insight into the protonation dynamics of the hydrogen-bonded networks. The connectivity and directionality of the networks are coupled to the conformation of an important protein residue in the channel, K362, rendering proton transfer in the entire channel feasible in only one of the two major conformations. Proton transport in the channel can thus be regulated by K362 not only through its possible role as a proton carrier itself, but also by allowing or preventing proton transport via water residues.

A Constant-pH Hybrid Monte Carlo Method with a Configuration-Selection Scheme Using the Zero Energy Difference Condition: Elucidation of Molecular Diffusivity Correlated with a pH-Dependent Solvation Shell.

We have proposed a new constant-pH (CpH) hybrid Monte Carlo (MC) method with a configuration-selection (CS) scheme, called the CS-CpH method, to obtain pH-dependent physical properties within a framework of atomistic molecular simulation. The CS-CpH method consists of carrying out a short equilibrium molecular dynamics (MD) and a searching MD coupled with thermostats and barostats to generate physically plausible configurations with changed protonation states (PSs) that are subsequently accepted or rejected according to the Metropolis MC procedure. As an example, we have applied it to glutamic acid in aqueous solution and have demonstrated that it can work to generate reasonably the pH-dependent microscopic configuration ensemble compatible with the experimental pKa value and also to show interestingly the molecular diffusivity correlated with pH-dependent solvation shell. In conclusion, we believe that the present CS-CpH method becomes a quite useful tool to study the microscopic origin of various pH-dependent phenomena, interpreting them in the atomistic chemical processes.

Binding selectivity of inhibitors toward the first over the second bromodomain of BRD4: theoretical insights from free energy calculations and multiple short molecular dynamics simulations.

Bromodomain-containing protein 4 (BRD4) plays an important role in mediating gene transcription involved in cancers and non-cancer diseases such as acute heart failure and inflammatory diseases. In this work, multiple short molecular dynamics (MSMD) simulations are integrated with a molecular mechanics generalized Born surface area (MM-GBSA) approach to decipher binding selectivity of three inhibitors 8NS, 82Y, and 837 toward two domains BD1 and BD2 of BRD4. The results demonstrate that the enthalpy effects play critical roles in selectivity identification of inhibitors toward BD1 and BD2, determining that 8NS has better selectivity toward BD2 than BD1, while 82Y and 837 more favorably bind to BD1 than BD2. A residue-based free-energy decomposition method was used to calculate an inhibitor–residue interaction spectrum and unveil contributions of separate residues to binding selectivity. The results identify six common residues, containing (P82, P375), (V87, V380), (L92, L385), (L94, L387), (N140, N433), and (I146, V439) individually belonging to (BD1, BD2) of BRD4, and yield a considerable binding difference of inhibitors to BD1 and BD2, suggesting that these residues play key roles in binding selectivity of inhibitors toward BD1 and BD2 of BRD4. Therefore, these results provide useful dynamics information and a structure affinity relationship for the development of highly selective inhibitors targeting BD1 and BD2 of BRD4.

How DNA affects the hyperthermophilic protein Ape10b2 for oligomerization: an investigation using multiple short molecular dynamics simulations.

Alba2 is a hyperthermophilic DNA-binding protein, and DNA plays a crucial role in the Alba2 oligomerization process. It is a pity that there is limited research in terms of how DNA affects the conformational change of Alba2 in oligomerization. Herein, we complement the crystal structure of the Ape10b2 (belongs to Alba2)-dsDNA complex (PDB ID: 3U6Y) and employ multiple short molecular dynamics (MSMD) simulations to illuminate the influence of DNA on Ape10b2 at four temperatures (300, 343, 363, and 373 K). Our results indicate that DNA could cause the conformational changes of two important regions (loop1 and loop5), which may be beneficial for protein oligomerization. The results of hydrogen bond analysis show that the increasing number of hydrogen bonds between two monomers of Ape10b2 may also be a favorable factor for oligomerization. In addition, Ape10b2 can stabilize DNA by electrostatic interactions with an increase in temperature, and five residues (Arg40, Arg42, Asn43, Asn45, and Arg46) play a stabilizing role during protein binding to DNA. Our findings could help in understanding the favorable factors leading to protein oligomerization, which contributes to enzyme engineering research from an industrial perspective.

Predicting substitutions to modulate disorder and stability in coiled-coils

BackgroundCoiled-coils are described as stable structural motifs, where two or more helices wind around each other. However, coiled-coils are associated with local mobility and intrinsic disorder. Intrinsically disordered regions in proteins are characterized by lack of stable secondary and tertiary structure under physiological conditions in vitro. They are increasingly recognized as important for protein function. However, characterizing their behaviour in solution and determining precisely the extent of disorder of a protein region remains challenging, both experimentally and computationally.ResultsIn this work, we propose a computational framework to quantify the extent of disorder within a coiled-coil in solution and to help design substitutions modulating such disorder. Our method relies on the analysis of conformational ensembles generated by relatively short all-atom Molecular Dynamics (MD) simulations. We apply it to the phosphoprotein multimerisation domains (PMD) of Measles virus (MeV) and Nipah virus (NiV), both forming tetrameric left-handed coiled-coils. We show that our method can help quantify the extent of disorder of the C-terminus region of MeV and NiV PMDs from MD simulations of a few tens of nanoseconds, and without requiring an extensive exploration of the conformational space. Moreover, this study provided a conceptual framework for the rational design of substitutions aimed at modulating the stability of the coiled-coils. By assessing the impact of four substitutions known to destabilize coiled-coils, we derive a set of rules to control MeV PMD structural stability and cohesiveness. We therefore design two contrasting substitutions, one increasing the stability of the tetramer and the other increasing its flexibility.ConclusionsOur method can be considered as a platform to reason about how to design substitutions aimed at regulating flexibility and stability.

Synthesis and in Silico Modelling of the Potential Dual Mechanistic Activity of Small Cationic Peptides Potentiating the Antibiotic Novobiocin against Susceptible and Multi-Drug Resistant Escherichia coli.

Cationic antimicrobial peptides have attracted interest, both as antimicrobial agents and for their ability to increase cell permeability to potentiate other antibiotics. However, toxicity to mammalian cells and complexity have hindered development for clinical use. We present the design and synthesis of very short cationic peptides (3–9 residues) with potential dual bacterial membrane permeation and efflux pump inhibition functionality. Peptides were designed based upon in silico similarity to known active peptides and efflux pump inhibitors. A number of these peptides potentiate the activity of the antibiotic novobiocin against susceptible Escherichia coli and restore antibiotic activity against a multi-drug resistant E. coli strain, despite having minimal or no intrinsic antimicrobial activity. Molecular modelling studies, via docking studies and short molecular dynamics simulations, indicate two potential mechanisms of potentiating activity; increasing antibiotic cell permeation via complexation with novobiocin to enable self-promoted uptake, and binding the E. coli RND efflux pump. These peptides demonstrate potential for restoring the activity of hydrophobic drugs.

How Well Does the Extended Linear Interaction Energy Method Perform in Accurate Binding Free Energy Calculations?

With continually increased computer power, molecular mechanics force field-based approaches, such as the endpoint methods of molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) and molecular mechanics generalized Born surface area (MM-GBSA), have been routinely applied in both drug lead identification and optimization. However, the MM-PB/GBSA method is not as accurate as the pathway-based alchemical free energy methods, such as thermodynamic integration (TI) or free energy perturbation (FEP). Although the pathway-based methods are more rigorous in theory, they suffer from slow convergence and computational cost. Moreover, choosing adequate perturbation routes is also crucial for the pathway-based methods. Recently, we proposed a new method, coined extended linear interaction energy (ELIE) method, to overcome some disadvantages of the MM-PB/GBSA method to improve the accuracy of binding free energy calculation. In this work, we have systematically assessed this approach using in total 229 protein-ligand complexes for eight protein targets. Our results showed that ELIE performed much better than the molecular docking and MM-PBSA method in terms of root-mean-square error (RMSE), correlation coefficient (R), predictive index (PI), and Kendall's τ. The mean values of PI, R, and τ are 0.62, 0.58, and 0.44 for ELIE calculations. We also explored the impact of the length of simulation, ranging from 1 to 100 ns, on the performance of binding free energy calculation. In general, extending simulation length up to 25 ns could significantly improve the performance of ELIE, while longer molecular dynamics (MD) simulation does not always perform better than short MD simulation. Considering both the computational efficiency and achieved accuracy, ELIE is adequate in filling the gap between the efficient docking methods and computationally demanding alchemical free energy methods. Therefore, ELIE provides a practical solution for the routine ranking of compounds in lead optimization.