Free Energy Approaches Research Articles

Molecular screening of phytocompounds targeting the interface between influenza A NS1 and TRIM25 to enhance host immune responses

BackgroundInfluenza A virus causes severe respiratory illnesses, especially in developing nations where most child deaths under 5 occur due to lower respiratory tract infections. The RIG-I protein acts as a sensor for viral dsRNA, triggering interferon production through K63-linked poly-ubiquitin chains synthesized by TRIM25. However, the influenza A virus's NS1 protein hinders this process by binding to TRIM25, disrupting its association with RIG-I and preventing downstream interferon signalling, contributing to the virus's evasion of the immune response. MethodsIn our study we used structural-based drug designing, molecular simulation, and binding free energy approaches to identify the potent phytocompounds from various natural product databases (>100,000 compounds) able to inhibit the binding of NS1 with the TRIM25. ResultsThe molecular screening identified EA-8411902 and EA-19951545 from East African Natural Products Database, NA-390261 and NA-71 from North African Natural Products Database, SA-65230 and SA- 4477104 from South African Natural Compounds Database, NEA- 361 and NEA- 4524784 from North-East African Natural Products Database, TCM-4444713 and TCM-6056 from Traditional Chinese Medicines Database as top hits. The molecular docking and binding free energies results revealed that these compounds have high affinity with the specific active site residues (Leu95, Ser99, and Tyr89) involved in the interaction with TRIM25. Additionally, analysis of structural dynamics, binding free energy, and dissociation constants demonstrates a notably stronger binding affinity of these compounds with the NS1 protein. Moreover, all selected compounds exhibit exceptional ADMET properties, including high water solubility, gastrointestinal absorption, and an absence of hepatotoxicity, while adhering to Lipinski’s rule. ConclusionOur molecular simulation findings highlight that the identified compounds demonstrate high affinity for specific active site residues involved in the NS1-TRIM25 interaction, exhibit exceptional ADMET properties, and adhere to drug-likeness criteria, thus presenting promising candidates for further development as antiviral agents against influenza A virus infections.

Kartograf: A Geometrically Accurate Atom Mapper for Hybrid-Topology Relative Free Energy Calculations.

Relative binding free energy (RBFE) calculations have emerged as a powerful tool that supports ligand optimization in drug discovery. Despite many successes, the use of RBFEs can often be limited by automation problems, in particular, the setup of such calculations. Atom mapping algorithms are an essential component in setting up automatic large-scale hybrid-topology RBFE calculation campaigns. Traditional algorithms typically employ a 2D subgraph isomorphism solver (SIS) in order to estimate the maximum common substructure. SIS-based approaches can be limited by time-intensive operations and issues with capturing geometry-linked chemical properties, potentially leading to suboptimal solutions. To overcome these limitations, we have developed Kartograf, a geometric-graph-based algorithm that uses primarily the 3D coordinates of atoms to find a mapping between two ligands. In free energy approaches, the ligand conformations are usually derived from docking or other previous modeling approaches, giving the coordinates a certain importance. By considering the spatial relationships between atoms related to the molecule coordinates, our algorithm bypasses the computationally complex subgraph matching of SIS-based approaches and reduces the problem to a much simpler bipartite graph matching problem. Moreover, Kartograf effectively circumvents typical mapping issues induced by molecule symmetry and stereoisomerism, making it a more robust approach for atom mapping from a geometric perspective. To validate our method, we calculated mappings with our novel approach using a diverse set of small molecules and used the mappings in relative hydration and binding free energy calculations. The comparison with two SIS-based algorithms showed that Kartograf offers a fast alternative approach. The code for Kartograf is freely available on GitHub (https://github.com/OpenFreeEnergy/kartograf). While developed for the OpenFE ecosystem, Kartograf can also be utilized as a standalone Python package.

Identification of oil palm cis-regulatory elements based on DNA free energy and single nucleotide polymorphism density

Transcription control through cis-regulatory elements (CREs) is one of important regulators of gene expression. This study aimed to identify the location of CREs in oil palm (Elaeis guineensis Jacq.) using the combination of DNA free energy and single nucleotide polymorphism (SNP) density approaches. Promoter region sequences were extracted oil palm genome spanning from 1500 nucleotides (nt) upstream to 1000 nt downstream of every annotated transcription start sites (TSS). Free energy profiles of each promoter region were calculated using PromPredict software. Raw reads from the deep sequencing of 59 oil palm origins were used to calculate SNP density of each promoter region. The result showed that the average free energy (AFE) on the upstream region of TSS is about 1.5 kcal/mol higher compared to the downstream region. Using DNA free energy method, 16,281 regions of CREs were predicted. Most of predicted CREs was located between 1 and 500 nt upstream of TSS. Anti-correlation pattern between free energy and SNP density was observed on the predicted regions of CREs. This anti-correlated pattern was also observed on an experimentally determined promoter of the oil palm metallothionein gene, EgMSP1. Considering the increasing use of promoter information on plant biotechnology, an easy and accurate promoter prediction using the combination of free energy and SNP density method could be recommended.

Novel Carbene Hydroxymethylene Derivatives: Gibbs Free Energy, NBO, AIM, and Hammett Approaches via DFT and MP2 Methods

Novel Carbene Hydroxymethylene Derivatives: Gibbs Free Energy, NBO, AIM, and Hammett Approaches via DFT and MP2 Methods

Principles of DNA cleavage in CRISPR-Cas9.

At the core of the CRISPR-Cas9 genome-editing technology, the endonuclease Cas9 introduces site-specific breaks in double-stranded DNA. In this system, the catalysis of target DNA cleavage by the highly flexible HNH domain is undefined, with two possible conformations whose catalytic relevance has remained unmet. Here, extensive molecular simulations - harnessing multi-microsecond molecular dynamics, quantum mechanics and free energy approaches - are combined with solution NMR and DNA cleavage assays to establish how the HNH nuclease is activated and cleaves the target DNA. We show that the conformation of the active state is critically dependent on the presence of Mg2+, unveiling its cardinal structural role in the organization of the active site. Solution NMR, DNA cleavage assays and molecular simulations of the Mg2+-bound HNH report on the formation of this active state, and consistently show that the protonation state of catalytic H840 is strongly affected by residue mutations within the active site. Finally, quantum mechanical simulations establish that DNA cleavage occurs through the identified active state, showing that the catalysis is activated by H840 and completed by K866, in line with DNA cleavage experiments. Overall, high-level computations, supported by experimental evaluation, resolve the catalytic mechanism and which of the known HNH conformations is responsible for target DNA cleavage in CRISPR-Cas9. This information helps enhancing the catalytic efficiency of Cas9 and its specificity, furthering the development of genome-editing tools.

Novel Carbene Hydroxymethylene Derivatives: Gibbs free energy, NBO, AIM, and Hammett approaches via DFT and MP2 methods

Density functional theory (DFT) and the Møller-Plesset expansion (MP2) calculations were benchmarked to characterize the structure of novel derivatives of carbene hydroxymethylene. In this regard, eleven numbers of primary aldehydes including formaldehyde, acetaldehyde, formyl chloride, 2-chloroacetaldehyde, formyl fluoride, 2-fluoroacetaldehyde, formic acid, propionaldehyde, glyoxal, methyl formate, and formyl bromide (1H-11Br) were scrutinized, at the B3LYP/6-311++G(3d,3p) level of theory. Thereafter, the process of keto-enol tautomerism via a [1,2]-H-shift of 1H-11Br was monitored to reach their corresponding enol forms (1P-11P). For all species (except for one structure), the TS involves a triangle structure of C-O-H and yields cis or trans isomers of a singlet ground-state (S) carbene that contrasts the multiplicity of ordinary carbenes. The bond dissociation energy (BDE) calculations were carried out for the homolytic cleavage of C−H bonds of 1H-11Br. The AIM, as a promising analysis that definitely reveals the nature of the bond, was conducted to characterize the eigenvalues at the bond critical points (BCPs). In addition, the relation between the Hammett equation and thermodynamic parameters was obtained. Our investigation suggests new stable S derivatives of carbene hydroxymethylene that can be isolated.

Surface free energy and roughness of flowable dental composites

Purpose The surface properties like roughness, wettability and surface free energy are important for utility properties of traditional and flowable dental restorative composites, due to their role in plaque formation, discoloration, mechanical wear or adhesion and bonding. The goal of our work was to assess the surface free energy (SFE) and the surface roughness (Ra) of three commercial flowable dental composites: everX Flow (bulk), everX Flow (dentin) and Flow-Art. Methods Surface roughness, contact angle and surface free energy were determined for tested composites. Two surface states (control and roughened) were compared. Roughness was measured with the use of the 3D optical profilometer. The contact angle (CA) was determined through the sessile drop method with the use of four different probing liquids. This enabled to apply two surface free energy approaches (Owens–Wendt (O-W) and van Oss–Chaudhury–Good (LWAB)). Additionally, Zisman’s approach (γC) was used. Results The water contact angle values were similar for Flow-Art (67.56±1.49°) and everX Flow (bulk) (68.94±2.72°) compared to higher value for everX Flow (dentin) (74.39±2.05°). SFE was in the range from 43 to 50 mJ/m^2 for O-W and from 47 to 62 mJ/m2 for LWAB. The γC was from 37 to 45 mJ/m^2. Conclusions Roughening composites’ surface influenced on increasing the CA value. All approaches of surface free energy calculations provide useful data for predicting interactions between flowable composites and dental tissues. Tested composites showed good wetting for initial state of surface after polymerization. These influence on better adhesion of the material to the bonding system during dental restoration.

MiRBind: A Deep Learning Method for miRNA Binding Classification

The binding of microRNAs (miRNAs) to their target sites is a complex process, mediated by the Argonaute (Ago) family of proteins. The prediction of miRNA:target site binding is an important first step for any miRNA target prediction algorithm. To date, the potential for miRNA:target site binding is evaluated using either co-folding free energy measures or heuristic approaches, based on the identification of binding 'seeds', i.e., continuous stretches of binding corresponding to specific parts of the miRNA. The limitations of both these families of methods have produced generations of miRNA target prediction algorithms that are primarily focused on 'canonical' seed targets, even though unbiased experimental methods have shown that only approximately half of in vivo miRNA targets are 'canonical'. Herein, we present miRBind, a deep learning method and web server that can be used to accurately predict the potential of miRNA:target site binding. We trained our method using seed-agnostic experimental data and show that our method outperforms both seed-based approaches and co-fold free energy approaches. The full code for the development of miRBind and a freely accessible web server are freely available.

Ab Initio Atomistic Simulations of Ca‐Perovskite Melting

AbstractMelting curves of Ca‐perovskite (pure CaSiO3) were determined by ab initio density functional theory, using two solid‐liquid coexistence methods and two free energy approaches, in the form of thermodynamic integration and two‐phase thermodynamics. The melting curves based on the solid‐liquid coexistence methods and thermodynamic integration rise steeply from 2000 K at 14 GPa to 7000 K at 136 GPa. The melting temperature at 136 GPa is 1400 K higher than previous ab initio predictions. The high thermal stability of Ca‐perovskite is linked to its high‐symmetry isometric structure and consistent with experiments, demonstrating that Ca‐perovskite is the most refractory phase in basaltic compositions in the lower mantle pressure range. The steep dT/dp slope of the melting curve also shows that the Ca‐perovskite liquidus field expands relative to those of bridgmanite and silica with increasing pressure, in agreement with experimental evidence from simple and complex systems.

Computational discovery of sulfonamide derivatives as potential inhibitors of the cruzain enzyme from T. cruzi by molecular docking, molecular dynamics and MM/GBSA approaches

ABSTRACT Chagas disease is caused by Trypanosoma cruzi (T. cruzi) protozoan and is considered by WHO as a neglected tropical disease, affecting almost 21 countries in the Americas. Until now, only two drugs are clinically recommended for its treatment, although these have limited efficacy and cause serious adverse reactions in the patients. Particularly, the Cruzain (Cz), a cysteine protease enzyme, has been established as a therapeutic target of T. cruzi. Here, the Cz binding mode of sulfonamide derivatives was investigated using fragment-based growing, molecular docking, molecular dynamics (MD) simulations and binding free energy approaches. Here, two new potent Cz inhibitors (named CP1 and CP4) were proposed from the previously evaluated dichlorothiophene-2-sulfonamide compound (named CP6). MD simulations show that CP1 and CP4 compounds were more stable than CP6. Besides, MM/GBSA approach suggests that new proposed compounds CP1 and CP4 (–26.10 and –23.81 kcal/mol, respectively) bind to Cz active site with lower binding free energy values than CP6 (–21.79 kcal/mol). These results suggested these two compounds could be candidate inhibitors against the Cz enzyme. Hence findings in our study can provide valuable information on the discovery of potent trypanocidal agents.

Development of OPLS-AA/M Parameters for Simulations of G Protein-Coupled Receptors and Other Membrane Proteins.

G protein-coupled receptors (GPCRs) and other membrane proteins are valuable drug targets, and their dynamic nature makes them attractive systems for study with molecular dynamics (MD) simulations and free energy approaches. Here, we report the development, implementation, and validation of OPLS-AA/M force field parameters to enable simulations of these systems. These efforts include the introduction of post-translational modifications including lipidations and phosphorylation. We also modify previously reported parameters for lipids to be more consistent with the OPLS-AA force field standard and extend their coverage. These new parameters are validated on a variety of test systems, with the results compared to high-level quantum mechanics calculations, experimental data, and simulations with other force fields. The results demonstrate that the new parameters reliably reproduce the behavior of membrane protein systems.

Thermodynamics up to the melting point in a TaVCrW high entropy alloy: Systematic ab initio study aided by machine learning potentials

Multi-principal-component alloys have attracted great interest as a novel paradigm in alloy design, with often unique properties and a vast compositional space auspicious for materials discovery. High entropy alloys (HEAs) belong to this class and are being investigated for prospective nuclear applications with reported superior mechanical properties including high-temperature strength and stability compared to conventional alloys. Computational materials design has the potential to play a key role in screening such alloys, yet for high-temperature properties, challenges remain in finding an appropriate balance between accuracy and computational cost. Here we develop an approach based on density-functional theory (DFT) and thermodynamic integration aided by machine learning based interatomic potential models to address this challenge. We systematically evaluate and compare the efficiency of computing the full free energy surface and thermodynamic properties up to the melting point at different stages of the thermodynamic integration scheme. Our new approach provides a $\ifmmode\times\else\texttimes\fi{}4$ speed-up with respect to comparable free energy approaches at the level of DFT, with errors on high-temperature free energy predictions less than 1 meV/atom. Calculations are performed on an equiatomic HEA, TaVCrW---a low-activation composition and therefore of potential interest for next generation fission and fusion reactors.

Understanding the Driving Forces That Trigger Mutations in SARS-CoV-2: Mutational Energetics and the Role of Arginine Blockers in COVID-19 Therapy.

SARS-CoV-2 is a global challenge due to its ability to mutate into variants that spread more rapidly than the wild-type virus. Because the molecular biology of this virus has been studied in such great detail, it represents an archetypal paradigm for research into new antiviral drug therapies. The rapid evolution of SARS-CoV-2 in the human population is driven, in part, by mutations in the receptor-binding domain (RBD) of the spike (S-) protein, some of which enable tighter binding to angiotensin-converting enzyme (ACE2). More stable RBD-ACE2 association is coupled with accelerated hydrolysis of furin and 3CLpro cleavage sites that augment infection. Non-RBD and non-interfacial mutations assist the S-protein in adopting thermodynamically favorable conformations for stronger binding. The driving forces of key mutations for Alpha, Beta, Gamma, Delta, Kappa, Lambda and Omicron variants, which stabilize the RBD-ACE2 complex, are investigated by free-energy computational approaches, as well as equilibrium and steered molecular dynamic simulations. Considered also are the structural hydropathy traits of the residues in the interface between SARS-CoV-2 RBD and ACE2 protein. Salt bridges and π-π interactions are critical forces that create stronger complexes between the RBD and ACE2. The trend of mutations is the replacement of non-polar hydrophobic interactions with polar hydrophilic interactions, which enhance binding of RBD with ACE2. However, this is not always the case, as conformational landscapes also contribute to a stronger binding. Arginine, the most polar and hydrophilic among the natural amino acids, is the most aggressive mutant amino acid for stronger binding. Arginine blockers, such as traditional sartans that bear anionic tetrazoles and carboxylates, may be ideal candidate drugs for retarding viral infection by weakening S-protein RBD binding to ACE2 and discouraging hydrolysis of cleavage sites. Based on our computational results it is suggested that a new generation of “supersartans”, called “bisartans”, bearing two anionic biphenyl-tetrazole pharmacophores, are superior to carboxylates in terms of their interactions with viral targets, suggesting their potential as drugs in the treatment of COVID-19. In Brief: This in silico study reviews our understanding of molecular driving forces that trigger mutations in the SARS-CoV-2 virus. It also reports further studies on a new class of “supersartans” referred to herein as “bisartans”, bearing two anionic biphenyltetrazole moieties that show potential in models for blocking critical amino acids of mutants, such as arginine, in the Delta variant. Bisartans may also act at other targets essential for viral infection and replication (i.e., ACE2, furin cleavage site and 3CLpro), rendering them potential new drugs for additional experimentation and translation to human clinical trials.

An in silico study to unveil potential drugs and vaccine chimera for HBV capsid assembly protein: combined molecular docking and dynamics simulation approach.

Humans are a major reservoir of the hepatitis B virus (HBV), therefore promising treatment and control vaccination strategies are needed to eradicate the virus. Though promising drugs and vaccines are available against HBV, still efforts are required to enrich the therapy options. Herein, the HBV assembly protein was explored to identify novel targets for future use against HBV. Computer-aided drug designing and immune-informatics techniques were employed for the identification of putative inhibitors and vaccine ensemble against HBV using capsid assembly protein. The identified drug molecule binds with high affinity to the active pocket of the protein, and several epitopes are scanned in the protein sequence. The drug molecule, besides being a good putative inhibitor, has acceptable drug-like properties. A multi-epitope vaccine is also constructed to overcome the limitations of weakly immunogenic epitopes. In contrast to the MHC II level, the set of predicted epitopes has been recognized to interact with significant numbers of HLA alleles of MHC I. Selected epitopes are extremely virulent, antigenic, nontoxic, nonallergic, have suitable affinity to bind with the prevailing DRB*0101 allele, and also spectacle 86% mediocre population coverage. A multi-epitope peptide-based vaccine chimera having 73 amino acids was designed. It emerged as substantially immunogenic, thermally stable, robust in producing cellular as well as humoral immune responses, and had competent physicochemical properties to analyze in vitro and in vivo studies. The capsid assembly protein is a in more stable nature in the presence of the drug molecule compared to the TLR3 receptor in the vaccine presence. These particulars were confirmed by exposing the docked molecules to absolute and relative binding free energy approaches of MMGBSA/PBSA. The purpose to investigate the interactions between the vaccine and a representative TLR3 immune receptor can reveal the intermolecular affinity and possible presentation mechanism of the vaccine by TLR3 to the host immune system. It was revealed that the vaccine is showing a very good affinity of binding for the TLR3 and forming a network of hydrophobic and hydrophilic interactions. Overall, the findings of this study are promising and might be useful for further experimental validations.

Advances and New Opportunities in the Rheology of Physically and Chemically Reversible Polymers

The advances in understanding the dynamics of both the physically and chemically reversible networks are summarized from a rheological perspective. The focus is placed on linear viscoelasticity, which is related to thermodynamics and reversible kinetics at quasi-equilibrium states. We first explain basic assumptions and predictions of the reversible-network theories, including the reversible gelation, sticky-Rouse, and sticky-reptation theories. We then summarize the dynamic features of the state-of-the-art materials that have not been fully incorporated into the current theoretical framework: (1) cooperative motion of the nearby stickers significantly increases the dissociation energy of physically reversible networks, (2) the degree of gelation becomes highly T-dependent when the Gibbs free energy approaches the thermal energy for both the physically and chemically reversible networks, and (3) the exchange rate of dynamic cross-links of chemically reversible networks is reaction-controlled and depends on the chemical species and concentrations of the reactants. Finally, some pathways for future theoretical development are suggested.

In Silico Core Proteomics and Molecular Docking Approaches for the Identification of Novel Inhibitors against Streptococcus pyogenes.

Streptococcus pyogenes is a significant pathogen that causes skin and upper respiratory tract infections and non-suppurative complications, such as acute rheumatic fever and post-strep glomerulonephritis. Multidrug resistance has emerged in S. pyogenes strains, making them more dangerous and pathogenic. Hence, it is necessary to identify and develop therapeutic methods that would present novel approaches to S. pyogenes infections. In the current study, a subtractive proteomics approach was employed to core proteomes of four strains of S. pyogenes using several bioinformatic software tools and servers. The core proteome consists of 1324 proteins, and 302 essential proteins were predicted from them. These essential proteins were analyzed using BLASTp against human proteome, and the number of potential targets was reduced to 145. Based on subcellular localization prediction, 46 proteins with cytoplasmic localization were chosen for metabolic pathway analysis. Only two cytoplasmic proteins, i.e., chromosomal replication initiator protein DnaA and two-component response regulator (TCR), were discovered to have the potential to be novel drug target candidates. Three-dimensional (3D) structure prediction of target proteins was carried out via the Swiss Model server. Molecular docking approach was employed to screen the library of 1000 phytochemicals against the interacting residues of the target proteins through the MOE software. Further, the docking studies were validated by running molecular dynamics simulation and highly popular binding free energy approaches of MM-GBSA and MM-PBSA. The findings revealed a promising candidate as a novel target against S. pyogenes infections.

Benchmark of Popular Free Energy Approaches Revealing the Inhibitors Binding to SARS-CoV-2 Mpro.

The COVID-19 pandemic has killed millions of people worldwide since its outbreak in December 2019. The pandemic is caused by the SARS-CoV-2 virus whose main protease (Mpro) is a promising drug target since it plays a key role in viral proliferation and replication. Currently, developing an effective therapy is an urgent task, which requires accurately estimating the ligand-binding free energy to SARS-CoV-2 Mpro. However, it should be noted that the accuracy of a free energy method probably depends on the protein target. A highly accurate approach for some targets may fail to produce a reasonable correlation with the experiment when a novel enzyme is considered as a drug target. Therefore, in this context, the ligand-binding affinity to SARS-CoV-2 Mpro was calculated via various approaches. The molecular docking approach was manipulated using Autodock Vina (Vina) and Autodock4 (AD4) protocols to preliminarily investigate the ligand-binding affinity and pose to SARS-CoV-2 Mpro. The binding free energy was then refined using the fast pulling of ligand (FPL), linear interaction energy (LIE), molecular mechanics-Poisson–Boltzmann surface area (MM-PBSA), and free energy perturbation (FEP) methods. The benchmark results indicated that for docking calculations, Vina is more accurate than AD4, and for free energy methods, FEP is the most accurate method, followed by LIE, FPL, and MM-PBSA (FEP > LIE ≈ FPL > MM-PBSA). Moreover, atomistic simulations revealed that the van der Waals interaction is the dominant factor. The residues Thr26, His41, Ser46, Asn142, Gly143, Cys145, His164, Glu166, and Gln189 are essential elements affecting the binding process. Our benchmark provides guidelines for further investigations using computational approaches.

Ensembler: A Simple Package for Fast Prototyping and Teaching Molecular Simulations.

Ensembler is a Python package that enables method prototyping using 1D and 2D model systems and allows deepening of the understanding of different molecular dynamics (MD) methods, starting from basic techniques to enhanced sampling and free-energy approaches. The ease of installing and using the package increases shareability, comparability, and reproducibility of scientific code developments. Here, we describe the implementation and usage of the package and provide an application example for free-energy calculation. The code of Ensembler is freely available on GitHub at https://github.com/rinikerlab/Ensembler.

Energy variation in diffusive void nucleation induced by electromigration

An energy approach is proposed to describe electromigration induced void nucleation based on phase transformation theory. The chemical potential for an individual migrated atom is predicted by diffusion induced back stress equivalent principle. After determining the chemical potential for the diffusing atoms, the Gibbs free energy controlling the void nucleation can be determined and the mass diffusion process is considered. The critical void radius and nucleation time are determined analytically when the Gibbs free energy approaches the extreme value. The theoretical predictions are compared with the experimental results from literatures and show good accuracy. The proposed model can also be applied to other diffusion induced damage processes such as thermomigration and stress migration.

Solubility Advantage of Amorphous Ketoprofen. Thermodynamic and Kinetic Aspects by Molecular Dynamics and Free Energy Approaches.

Thermodynamic and kinetic aspects of crystalline (c-KTP) and amorphous (a-KTP) ketoprofen dissolution in water have been investigated by molecular dynamics simulation focusing on free energy properties. Absolute free energies of all relevant species and phases have been determined by thermodynamic integration on a novel path, first connecting the harmonic to the anharmonic system Hamiltonian at low T and then extending the result to the temperature of interest. The free energy required to transfer one ketoprofen molecule from the crystal to the solution is in fair agreement with the experimental value. The absolute free energy of the amorphous form is 19.58 kJ/mol higher than for the crystal, greatly enhancing the ketoprofen concentration in water, although as a metastable species in supersaturated solution. The kinetics of the dissolution process has been analyzed by computing the free energy profile along a reaction coordinate bringing one ketoprofen molecule from the crystal or amorphous phase to the solvated state. This computation confirms that, compared to the crystal form, the dissolution rate is nearly 7 orders of magnitude faster for the amorphous form, providing one further advantage to the latter in terms of bioavailability. The problem of drug solubility, of great practical importance, is used here as a test bed for a refined method to compute absolute free energies, which could be of great interest in biophysics and drug discovery in particular.