The work, published in Nature by Chenyu Tang, Mayank Prakash Pandey and colleagues at the Université de Lorraine and CNRS in Nancy, France, with collaborators in Madrid, Chicago and Urbana-Champaign, addresses a stubborn bottleneck in computational biology. Standard molecular dynamics simulations propagate atoms step by step according to the laws of classical physics, and they do so faithfully. But the transitions that matter most, such as protein folding, allosteric signaling and membrane transport, are rare events. A simulation might churn through millions of time steps without ever witnessing the molecule cross from one stable state to another, because the probability of stumbling into the transition region by chance is vanishingly small.

Computational scientists have long responded with enhanced-sampling strategies, which apply artificial forces or cleverly designed biases to push molecules over energetic barriers. These methods have produced genuine insights, but they come with serious caveats. They are computationally demanding, and they typically require the researcher to choose, in advance, a set of collective variables, essentially a guess about which combination of atomic coordinates describes the reaction. If that guess is wrong or incomplete, the resulting free-energy landscape and apparent mechanism can be distorted. A recent study cited by the team put the problem bluntly in its title: convergence is not correctness, and enhanced-sampling methods can perform inconsistently as biological complexity increases.

Gen-COMPAS takes a different route. The framework couples a denoising diffusion probabilistic model, the same class of generative architecture that powers modern image synthesis, with a rigorous statistical concept from transition state theory known as the committor. The diffusion model is trained to produce structurally plausible intermediate conformations that sit between the two known end points of a transition. These generated structures are not accepted on faith. Instead, they are filtered using committor-based criteria, which identify the configurations from which a molecule is equally likely to fall forward to the product state or backward to the reactant state, the hallmark of a true transition state.

The committor is a powerful but demanding quantity. For any given molecular configuration, it answers a deceptively simple question: if the system were released from this point with random thermal velocities, what is the probability that it would commit to the product rather than the reactant? Configurations with a committor of one half define the transition state ensemble. Estimating this probability traditionally requires launching many short trajectories from each candidate configuration, which is exactly the kind of brute-force calculation that makes rare-event statistics so expensive. Gen-COMPAS turns this into an iterative loop: the generative model proposes candidate intermediates, short unbiased simulations from those intermediates test whether they genuinely belong to the transition region, and the results refine both the model and the ensemble.

The payoff is a dramatic compression of the required sampling. According to the authors, short unbiased simulations launched from the generated intermediates yield transition-region ensembles at aggregate sampling scales ranging from nanoseconds to submicroseconds, whereas conventional approaches would require orders of magnitude more simulation time to assemble equivalent statistics. In practical terms, the method replaces the hopeless task of waiting for a rare event to happen spontaneously with the tractable task of verifying that generated candidate structures are genuine members of the transition ensemble.

To demonstrate the framework, the team applied it to three systems of increasing size and biological complexity. The first was Trp-cage, a miniprotein of roughly twenty residues that has become a standard benchmark for folding studies because it folds quickly enough to be simulated directly. Decades of work, including landmark long-trajectory studies of fast-folding proteins, have mapped its folding landscape in detail, giving the team an independent yardstick against which to judge their results. Gen-COMPAS recovered committors, transition states and free-energy landscapes consistent with this established knowledge, starting only from the folded and unfolded end-point structures.

The second test system was the mitochondrial ADP/ATP carrier, a membrane transporter that shuttles the energy currency ATP across the inner mitochondrial membrane in exchange for ADP. This protein operates through a switching mechanism between conformational states that has been studied with structural biology and a variety of simulation techniques, but its full transition pathway remains challenging to sample because the carrier is embedded in a lipid membrane and undergoes large-scale domain movements. Gen-COMPAS reconstructed the transition pathway between the carrier’s known states without any predefined reaction coordinate, offering a mechanistic picture that previously would have demanded extensive biased simulations and careful, potentially contentious, choices of collective variables.

The third and most ambitious application was the nicotinic acetylcholine receptor, a pentameric ligand-gated ion channel that mediates synaptic transmission. Channel activation involves coordinated conformational changes across five protein subunits, and recent work has suggested that these subunit transitions occur asynchronously, priming the receptor for activation in a stepwise fashion. Simulating the full gating transition of such a large membrane protein is far beyond the reach of straightforward molecular dynamics. Gen-COMPAS nevertheless recovered the transition pathway and free-energy landscape from the known resting and activated structures alone, with no prior mechanistic knowledge encoded into the calculation.

The significance of the method lies not only in its speed but in what it does not require. No collective variables need to be chosen by hand, no reaction coordinates need to be guessed, and no prior hypothesis about the mechanism needs to be supplied. The generative model and the committor filter together let the data define the pathway. This removes one of the most persistent sources of bias in enhanced sampling, the researcher’s own assumptions about how the transition ought to proceed, and it opens the door to systems where no expert intuition exists to guide variable selection.

The method also arrives amid a broader convergence of machine learning and molecular simulation. Generative models have been used to emulate equilibrium protein ensembles, to sample molecular dynamics trajectories directly, and to guide transition path sampling in systems ranging from lipid flip-flop to membrane nanoporation. Gen-COMPAS distinguishes itself by combining generative proposal with a physically rigorous acceptance criterion rooted in transition rate theory, ensuring that the structures it produces are not merely plausible but statistically meaningful members of the true transition ensemble. The authors acknowledge prior machine-guided path-sampling work and committor-learning approaches as foundations for this synthesis.

Accessibility may prove as important as performance. Gen-COMPAS is released as an installable open-source package on GitHub, complete with a unified command-line interface, ready-to-use configuration files, molecular-simulation inputs for representative systems, and a graphical user interface that guides users through the available options. Input files for all systems analyzed in the paper are publicly available, and source data accompany the publication. The team also gratefully acknowledges reference molecular dynamics trajectories provided by D. E. Shaw Research, whose specialized hardware has long set the benchmark for long unbiased simulations that methods like this one can now be validated against.

For the field, the implications extend across drug discovery, basic biophysics and beyond. Ion channels and membrane transporters are among the most heavily targeted protein families in pharmacology, and understanding the exact pathway by which they switch states can reveal where small molecules might bind to stabilize one state or another. Allosteric transitions in signaling proteins, another class of rare events, could similarly be mapped without the guesswork that has historically limited such studies. If the framework generalizes as its demonstration systems suggest, the computational microscope that biophysicists have been building for half a century may finally be able to zoom in on the fleeting moments that matter most, the instants in which a molecule decides what it will become next.

Subject of Research: Generative committor-guided path sampling of biomolecular conformational transitions

Article Title: Breaking timescales with generative sampling of conformational transitions

Article References: Breaking timescales with generative sampling of conformational transitions. (n.d.). https://doi.org/10.1038/s41586-026-11025-1

Image Credits: AI Generated

DOI: 10.1038/s41586-026-11025-1

Keywords: Gen-COMPAS, molecular dynamics, transition path sampling, denoising diffusion model, committor, protein folding, enhanced sampling, ion channel, membrane transporter, free-energy landscape, machine learning, computational biophysics

The idea that black holes possess genuine thermodynamic properties dates back to the foundational work of Jacob Bekenstein, Stephen Hawking and their collaborators, who established that black holes carry a well-defined temperature and entropy proportional to the area of their event horizons. That analogy, however, raised a deeper question: do black holes exhibit the full machinery of thermodynamics, including phase structure and critical phenomena? Research beginning in the 1970s by P.C.W. Davies and P. Hut suggested they might, and the discovery gained fresh momentum after Juan Maldacena’s 1997 AdS/CFT correspondence made asymptotically AdS black holes central to modern theoretical physics.

A pivotal conceptual advance came with the reinterpretation of the cosmological constant as a thermodynamic pressure. In this extended framework, the phase behaviour of charged and rotating AdS black holes closely mirrors the van der Waals liquid-gas transition of ordinary fluids: a small black hole phase corresponds roughly to the gas, a large black hole phase to the liquid, and a first-order transition connects them, complete with a critical point where the distinction between the phases dissolves. The new study asks what happens to the dynamics of such transitions as the critical point is approached, treating the black hole not as a static equilibrium object but as a stochastic system buffeted by thermal fluctuations.

The researchers build on a framework known as free energy landscape dynamics, which has proven powerful in physics, chemistry and biology, from protein folding to chemical reactions. In this picture, the thermodynamic states of a system are valleys on a landscape defined by its free energy, and thermal noise jiggles the system between them. Previously, Rong-Gen Li and Jin Wang and their collaborators applied this framework to the Hawking-Page transition and to the small-large black hole transition of Reissner-Nordström AdS (RN-AdS) black holes, showing that stochastic switching between phases can occur in both directions. More recently, it was shown that near the critical and spinodal points of the RN-AdS system, the relaxation dynamics slows dramatically.

The Dibrugarh team extends this analysis to rotating Kerr-AdS black holes, and in doing so makes a deliberate technical choice: rather than using the horizon radius as the fluctuating order parameter, they treat the black hole’s entropy as the dynamical variable evolving on the free energy landscape. For Kerr-AdS black holes, the generalized Gibbs free energy in the canonical ensemble is most naturally written as a function of the entropy, and the entropy uniquely labels each equilibrium macrostate. The authors also show that this choice does not affect the universal long-time behaviour, because entropy and horizon radius are related by a smooth transformation that merely relabels coordinates on the same thermodynamic manifold.

The mathematical core of the analysis is a Langevin equation: a stochastic differential equation in which the entropy evolves under a deterministic driving force generated by the slope of the free energy, a friction term describing dissipation into the thermal environment, and a Gaussian white noise term constrained by the fluctuation-dissipation relation. Far from criticality, the free energy well surrounding a stable state is approximately parabolic, and perturbations decay exponentially with a characteristic time given by the damping coefficient divided by the curvature of the free energy at equilibrium. At the critical point, however, the first three derivatives of the free energy with respect to entropy vanish, and the quadratic approximation collapses entirely.

That collapse is the origin of critical slowing down. As the landscape flattens, the restoring force that pulls fluctuations back toward equilibrium weakens and eventually disappears, so fluctuations persist for ever longer times. The researchers demonstrate this both analytically and numerically. Simulating the Langevin equation with a Heun predictor-corrector scheme, they extract the autocorrelation time and the variance of the entropy trajectories, finding that both rise sharply near the critical point and near the spinodal lines where one of the black hole phases ceases to exist. Independently, they solve the associated Fokker-Planck equation, which describes the probability distribution of the entropy, and find that its smallest nonzero eigenvalue, which sets the slowest relaxation rate, is strongly suppressed near criticality, confirming the same physics from the spectral side.

The study’s most striking result concerns universality. Fitting the numerically obtained relaxation time to a power law of the form tau proportional to the reduced distance from criticality raised to a negative exponent, the team recovers a dynamical critical exponent of approximately two-thirds along every path they examined, whether varying the temperature at fixed pressure or angular momentum, varying the pressure at fixed temperature, or varying the angular momentum at fixed temperature, and regardless of whether the critical point is approached from above or below. Analytically, this exponent follows from a mean-field argument: near a critical inflection point the order parameter scales as the cube root of the distance from criticality, so the free energy curvature scales as the two-thirds power, and the relaxation time, its inverse, diverges as the minus two-thirds power.

Remarkably, the same exponent emerges for three physically distinct black hole families: charged RN-AdS black holes, rotating Kerr-AdS black holes, and Bardeen black holes, an early example of regular black holes whose cores are nonsingular and which satisfy the weak energy condition. Despite radically different spacetime geometries and thermodynamic variables, all three systems realize identical dynamical scaling, placing them in the same mean-field dynamical universality class. The conclusion is that critical slowing down is governed not by the microscopic details of the black hole solution but by the generic structure of the free energy landscape, specifically its quartic form at a critical inflection point, echoing the logic of universality that underpins conventional critical phenomena.

The work connects black hole thermodynamics to a web of ideas spanning the Kibble-Zurek mechanism of defect formation in cosmological phase transitions, early-warning signals of critical transitions in complex systems, and the time-dependent Ginzburg-Landau theory of near-critical dynamics. The authors suggest several future directions, including extensions to higher-dimensional black holes, modified gravity theories and multicritical systems, and possible links to quasinormal modes, thermodynamic geometry, Lyapunov exponents and holographic nonequilibrium phenomena. For now, the message is conceptually simple and profound: a black hole poised at a thermodynamic critical point forgets its past ever more slowly, and the way it forgets obeys a law that charged, rotating and even regular black holes all share. In the slow drift toward a phase transition, gravity and statistical mechanics appear to speak the same universal language.

Subject of Research: Universal dynamical scaling and critical slowing down in black hole phase transitions in anti-de Sitter spacetime

Article Title: Critical slowing down of black hole phase transition and universal dynamic scaling in AdS black holes

Article References: Awal, M. B., & Phukon, P. (2026). Critical slowing down of black hole phase transition and universal dynamic scaling in AdS black holes. The European Physical Journal C, 86(9), Article 1058. https://doi.org/10.1140/epjc/s10052-026-16329-0

Image Credits: AI Generated

DOI: 10.1140/epjc/s10052-026-16329-0

Keywords: black hole thermodynamics, phase transitions, critical slowing down, AdS black holes, free energy landscape, Langevin equation, Fokker-Planck equation, Kerr-AdS black holes, RN-AdS black holes, Bardeen black holes, universality, dynamical critical exponent

The research, led by Hafs Essaadi and colleagues at Mohammed V University in Rabat, Morocco, together with collaborators at the Mohammed VI Center for Research and Innovation and UM6SS, focused on the viral 3C protease, an enzyme designated 3Cpro that is indispensable to the rhinovirus life cycle. After the virus infects a cell, its genome is translated into a single long polyprotein that must be cleaved into functional viral proteins, and the 3C protease performs most of these cuts. Because the catalytic architecture of 3Cpro is highly conserved across human rhinovirus species, a molecule that disables it could in principle suppress a wide range of rhinovirus strains, making the enzyme an attractive therapeutic target.

To find candidate inhibitors, the team screened a library of 49,437 compounds against the 3C protease of human rhinovirus species C, the rhinovirus group most frequently associated with severe asthma exacerbations. The docking calculations were carried out with AutoDock Vina, a widely used molecular docking engine that predicts how small molecules orient themselves within a protein binding pocket and estimates the binding affinity of each pose. The researchers then applied ADMET-based prioritization, filtering the top-scoring hits for acceptable absorption, distribution, metabolism, excretion, and toxicity profiles, a step designed to weed out compounds that might bind well in silico but fail as drug candidates.

The docking analysis converged on two lead compounds that occupied the catalytic pocket of the protease in orientations predicted to be highly favorable. Both molecules formed stabilizing interactions with key residues of the active site, including His40, Glu71, and Cys147, the latter being the catalytic cysteine that sits at the heart of the enzyme’s cleavage chemistry. When compared with rupintrivir, the best-known experimental rhinovirus 3C protease inhibitor, which functions as an irreversible covalent inhibitor, the two new compounds achieved comparable or better engagement of the catalytic site without forming covalent bonds, a property that could translate into improved safety profiles.

Docking scores alone are a crude measure of binding, so the team subjected the protein-ligand complexes to molecular dynamics simulations lasting 200 nanoseconds each, allowing the atoms to move under realistic physical forces and revealing whether the predicted binding poses remain stable over time. Both compounds stabilized the protease relative to the unbound, or apo, form of the enzyme. Compound 1 produced the lowest protein root-mean-square deviation, holding the overall structure of the protease at 1.21 plus or minus 0.22 angstroms from its starting conformation, while compound 2 yielded the lowest root-mean-square fluctuation for the critical Cys147 residue, at just 0.48 angstroms, indicating that the catalytic nucleophile itself was held unusually rigid in the presence of this ligand.

Ligand mobility within the binding pocket provided further evidence of durable binding. Over the course of the simulations, compound 1 displayed a ligand RMSD of 1.49 plus or minus 0.78 angstroms and compound 2 a value of 2.01 plus or minus 0.39 angstroms, whereas rupintrivir wandered considerably more, with a ligand RMSD of 4.83 plus or minus 0.89 angstroms. Lower ligand RMSD values indicate that a molecule stays anchored in its original binding pose rather than drifting or partially exiting the pocket, suggesting that the two new candidates maintain more persistent contact with the active site than the reference inhibitor under dynamic conditions.

To quantify binding strength more rigorously, the researchers applied the MM/PBSA method, which combines molecular mechanics energies with solvation models to estimate the free energy of binding from simulated trajectories. Both leads outperformed rupintrivir on this metric: compound 1 achieved an effective binding energy of minus 23.59 plus or minus 6.87 kilocalories per mole, and compound 2 reached minus 25.25 plus or minus 5.75 kilocalories per mole, compared with minus 19.43 plus or minus 4.46 kilocalories per mole for rupintrivir. The team complemented these calculations with free energy landscape analysis, a technique that maps the conformational states sampled during simulation and identifies the most thermodynamically stable configurations of each complex, providing an additional layer of confidence that the observed binding modes represent genuine energetic minima rather than transient artifacts.

The significance of a non-covalent mechanism deserves emphasis. Rupintrivir, which reached phase II clinical trials as a nasal spray, irreversibly modifies the catalytic cysteine, and while this reactivity underlies its potency, covalent inhibitors can raise concerns about off-target modification of human enzymes that rely on similar cysteine chemistry. Compounds that achieve strong binding through reversible, non-covalent interactions, as the two leads reported here appear to do, may offer a wider therapeutic window. The ADMET filtering applied during the study further suggests that the candidates were selected not only for potency but also for drug-like behavior, although the authors stress that computational predictions of this kind require experimental confirmation.

Indeed, the study stops short of laboratory validation, and the authors are explicit that compounds 1 and 2 should be regarded as promising candidates for further experimental testing rather than proven antivirals. Enzyme inhibition assays, antiviral activity measurements in infected cell cultures, and eventually pharmacokinetic and toxicity studies in vivo will be needed to determine whether the computational promise translates into real therapeutic value. The data generated during the study are available from the corresponding author upon request, and the work was supported in part by computational resources from the Pediatric Translational Clinical Research Unit.

Nevertheless, the study adds to a growing body of evidence that structure-based computational screening can accelerate antiviral discovery against rhinoviruses, a pathogen family that has historically frustrated drug developers because of its antigenic diversity. By anchoring the search on a conserved, essential enzyme and validating hits through a multi-layered pipeline of docking, long-timescale dynamics, free energy landscape mapping, MM/PBSA energetics, and ADMET profiling, the Moroccan team has delivered a shortlist of chemically tractable starting points. If subsequent experiments bear out the predicted potency of these molecules, the work could represent an early but meaningful step toward the first effective antiviral treatment for the infections that trigger many of the world’s asthma attacks and common colds.

Subject of Research: Computational discovery of non-covalent inhibitors targeting the human rhinovirus 3C protease

Article Title: Computational discovery of novel human rhinovirus 3 C protease inhibitors: molecular docking, dynamic simulations, free energy landscape, MMPBSA and ADMET analysis

Article References: Essaadi, H., Chourir, A., Kourou, J., Makhloufi, F., Hachlaf, O., Abidou, A., Boutayeb, S., Eljaoudi, R., Belyamani, L., Ibrahimi, A., Hakmi, M., & Hafidi, N. E. (2026). Computational discovery of novel human rhinovirus 3 C protease inhibitors: molecular docking, dynamic simulations, free energy landscape, MMPBSA and ADMET analysis. Molecular Diversity. https://doi.org/10.1007/s11030-026-11704-1

Image Credits: AI Generated

DOI: 10.1007/s11030-026-11704-1

Keywords: human rhinovirus, 3C protease, antiviral drug discovery, molecular docking, molecular dynamics simulations, MM/PBSA, free energy landscape, ADMET analysis, virtual screening, rupintrivir, common cold, asthma exacerbation