Fast Dynamic Docking Guided by Adaptive Electrostatic Bias: The MD-Binding Approach

Engineering chemical entities to modify how pharmaceutical targets function, as it is done in drug design, requires a good understanding of molecular recognition and binding. In this context, the limitations of statically describing bimolecular recognition, as done in docking/scoring, call for insightful and efficient dynamical investigations. On the experimental side, the characterization of dynamical binding processes is still in its infancy. Thus, computer simulations, particularly molecular dynamics (MD), are compelled to play a prominent role, allowing a deeper comprehension of the binding process and its causes and thus a more informed compound selection, making more significant the computational contribution to drug discovery (Carlson, H. A. Curr. Opin. Chem. Biol. 2002, 6, 447-452). Unfortunately, MD-based approaches cannot yet describe complex events without incurring prohibitive time and computational costs. Here, we present a new method for fully and dynamically simulating drug-target-complex formations, tested against a real world and pharmaceutically relevant benchmark set. The method, based on an adaptive, electrostatics-inspired bias, envisions a campaign of trivially parallel short MD simulations and a strategy to identify a near native binding pose from the sampled configurations. At an affordable computational cost, this method provided predictions of good accuracy also when the starting protein conformation was different from that of the crystal complex, a known hurdle for traditional molecular docking (Lexa, K. W.; Carlson, H. A. Q. Rev. Biophys. 2012, 45, 301-343). Moreover, along the observed binding routes, it identified some key features also found by much more computationally expensive plain-MD simulations. Overall, this methodology represents significant progress in the description of binding phenomena

Explanation is All You Need in Distillation: Mitigating Bias and Shortcut Learning

Bias and spurious correlations in data can cause shortcut learning, undermining out-of-distribution (OOD) generalization in deep neural networks. Most methods require unbiased data during training (and/or hyper-parameter tuning) to counteract shortcut learning. Here, we propose the use of explanation distillation to hinder shortcut learning. The technique does not assume any access to unbiased data, and it allows an arbitrarily sized student network to learn the reasons behind the decisions of an unbiased teacher, such as a vision-language model or a network processing debiased images. We found that it is possible to train a neural network with explanation (e.g by Layer Relevance Propagation, LRP) distillation only, and that the technique leads to high resistance to shortcut learning, surpassing group-invariant learning, explanation background minimization, and alternative distillation techniques. In the COLOURED MNIST dataset, LRP distillation achieved 98.2% OOD accuracy, while deep feature distillation and IRM achieved 92.1% and 60.2%, respectively. In COCO-on-Places, the undesirable generalization gap between in-distribution and OOD accuracy is only of 4.4% for LRP distillation, while the other two techniques present gaps of 15.1% and 52.1%, respectively

Addendum to BiKi Life Sciences: A New Suite for Molecular Dynamics and Related Methods in Drug Discovery

The full version of BiKi Life Sciences suite of software is available for academics for 2 years after publication1 at a nominal fee

Kinetics of protein-ligand unbinding via smoothed potential molecular dynamics simulations

Drug discovery is expensive and high-risk. Its main reasons of failure are lack of efficacy and toxicity of a drug candidate. Binding affinity for the biological target has been usually considered one of the most relevant figures of merit to judge a drug candidate along with bioavailability, selectivity and metabolic properties, which could depend on off-target interactions. Nevertheless, affinity does not always satisfactorily correlate with in vivo drug efficacy. It is indeed becoming increasingly evident that the time a drug spends in contact with its target (aka residence time) can be a more reliable figure of merit. Experimental kinetic measurements are operatively limited by the cost and the time needed to synthesize compounds to be tested, to express and purify the target, and to setup the assays. We present here a simple and efficient molecular-dynamics-based computational approach to prioritize compounds according to their residence time. We devised a multiple-replica scaled molecular dynamics protocol with suitably defined harmonic restraints to accelerate the unbinding events while preserving the native fold. Ligands are ranked according to the mean observed scaled unbinding time. The approach, trivially parallel and easily implementable, was validated against experimental information available on biological systems of pharmacological relevance

Fingerprint-enhanced capacitive-piezoelectric flexible sensing skin to discriminate static and dynamic tactile stimuli

nspired by the structure and functions of the human skin, a highly sensitive capacitive‐piezoelectric flexible sensing skin with fingerprint‐like patterns to detect and discriminate between spatiotemporal tactile stimuli including static and dynamic pressures and textures is presented. The capacitive‐piezoelectric tandem sensing structure is embedded in the phalange of a 3D‐printed robotic hand, and a tempotron classifier system is used for tactile exploration. The dynamic tactile sensor, interfaced with an extended gate configuration to a common source metal oxide semiconductor field effect transistor (MOSFET), exhibits a sensitivity of 2.28 kPa−1. The capacitive sensing structure has nonlinear characteristics with sensitivity varying from 0.25 kPa−1 in the low‐pressure range (<100 Pa) to 0.002 kPa−1 in high pressure (≈2.5 kPa). The output from the presented sensor under a closed‐loop tactile scan, carried out with an industrial robotic arm, is used as latency‐coded spike trains in a spiking neural network (SNN) tempotron classifier system. With the capability of performing a real‐time binary naturalistic texture classification with a maximum accuracy of 99.45%, the presented bioinspired skin finds applications in robotics, prosthesis, wearable sensors, and medical devices

Role of Muscle Synergies in Real-Time Classification of Upper Limb Motions using Extreme Learning Machines

10.1186/s12984-016-0183-0Journal of NeuroEngineering and Rehabilitation13118

Fast and Memory-Efficient Import Vector Domain Description

One-class learning is a classical and hard computational intelligence task. In the literature, there are some effective and powerful solutions to address the problem. There are examples in the kernel machines realm, Support Vector Domain Description, and the recently proposed Import Vector Domain Description (IVDD), which directly delivers the sample probability of belonging to the class. Here, we propose and discuss two optimization techniques for IVDD to significantly improve the memory footprint and consequently to scale to datasets that are larger than the original formulation. We propose two strategies. First, we propose using random features to approximate the gaussian kernel together with a primal optimization algorithm. Second, we propose a Nystr\uf6m-like approximation of the functional together with a fast converging and accurate self-consistent algorithm. In particular, we replace the a posteriori sparsity of the original optimization method of IVDD by randomly selecting a priori landmark samples in the dataset. We find this second approximation to be superior. Compared to the original IVDD with the RBF kernel, it achieves high accuracy, is much faster, and grants huge memory savings