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Early-stage drug discovery has been limited by initial hit identification and lead optimization and their associated costs (1). Ultra-large virtual screens (ULVSs), which involve the virtual evaluation of massive numbers of molecules to engage a macromolec-ular target, have the ability to significantly alleviate these problems, as was recently demonstrated in multiple studies (2–7). Despite their potential, ULVSs have so far only explored a tiny fraction of the chemical space and of available docking programs. Here, we present VirtualFlow 2.0, the next generation of the first open-source drug discovery platform dedicated to ultra-large virtual screen ings. VirtualFlow 2.0 provides the REAL Space from Enamine containing 69 billion drug-like molecules in a "ready-to-dock" format, the largest library of its kind available to date. We provide an 18-dimensional matrix for intuitive exploration of the library through a web interface, where each dimension corresponds to a molecular property of the ligands. Additionally, VirtualFlow 2.0 supports multiple techniques that dramatically reduce computational costs, including a new method called Adaptive Target-Guided Virtual Screening (ATG-VS). By sampling a representative sparse version of the library, ATG-VS identifies the sections of the ultra-large chemical space that harbors the highest potential to engage the target site, leading to substantially reduced computational costs by up to a factor of 1000. In addition, VirtualFlow 2.0 supports the latest deep learning and GPU-based docking methods, allowing further speed-ups by up to two orders of magnitude. VirtualFlow 2.0 supports 1500 unique docking methods providing target-specific and consensus docking options to increase accuracy and has the ability to screen new types of ligands (such as peptides) and target receptors (including RNA and DNA). Moreover, VirtualFlow 2.0 has many advanced new features, such as enhanced AI and cloud support. We demonstrate a perfectly linear scaling behavior up to 5.6 million CPUs in the AWS Cloud, a new global record for parallel cloud computing. Due to its open-source nature and versatility, we expect that VirtualFlow 2.0 will play a key role in the future of early-stage drug discovery.

Clinical gait analysis is an important biomechanics field that is often influenced by subjectivity in time-varying analysis leading to type I and II errors. Statistical Parametric Mapping can operate on all time-varying joint dynamics simultaneously, thereby overcoming subjectivity errors. We present MovementRx, the first gait analysis modelling application that correctly models the deviations of joints kinematics and kinetics both in 3 and 1 degrees of freedom; presented with easy-to-understand color maps for clinicians with limited statistical training. MovementRx is a python-based versatile GUI-enabled movement analysis decision support system, that provides a holistic view of all lower limb joints fundamental to the kinematic/kinetic chain related to functional gait. The user can cascade the view from single 3D multivariate result down to specific single joint individual 1D scalar movement component in a simple, coherent, objective, and visually intuitive manner. We highlight MovementRx benefit by presenting a case-study of a right knee osteoarthritis (OA) patient with otherwise undetected postintervention contralateral OA predisposition. MovementRx detected elevated frontal plane moments of the patient's unaffected knee. The patient also revealed a surprising adverse compensation to the contralateral limb. Agency for Science, Technology and Research (A*STAR) Nanyang Technological University Published version Rehabilitation Research Institute of Singapore (RRIS) is funded by tripartite funding: Agency for Science, Technology and Research (A-STAR), National Health Group (NHG), and Nanyang Technological University (NTU Singapore). Tis work is part of the Ability data project in RRIS.

SPM is a statistical method of analysis of time-varying human movement gait signal, depending on the random field theory (RFT). MovementRx is our inhouse-developed decision-support system that depends on SPM1D Python implementation of the SPM (spm1d.org). We present the potential application of MovementRx in the prediction of increased joint forces with the possibility to predispose to osteoarthritis in a sample of post-surgical Transtibial Amputation (TTA) patients who were ambulant in the community. We captured the three-dimensional movement profile of 12 males with TTA and studied them using MovementRx, employing the SPM1D Python library to quantify the deviation(s) they have from our corresponding reference data, using "Hotelling 2" and "T test 2" statistics for the 3D movement vectors of the 3 main lower limb joints (hip, knee, and ankle) and their nine respective components (3 joints × 3 dimensions), respectively. MovementRx results visually demonstrated a clear distinction in the biomechanical recordings between TTA patients and a reference set of normal people (ABILITY data project), and variability within the TTA patients' group enabled identification of those with an increased risk of developing osteoarthritis in the future. We conclude that MovementRx is a potential tool to detect increased specific joint forces with the ability to identify TTA survivors who may be at risk for osteoarthritis. Agency for Science, Technology and Research (A*STAR) Nanyang Technological University Published version The Rehabilitation Research Institute of Singapore (RRIS) is funded by tripartite funding: The Agency for Science, Technology and Research (A-STAR), the National Healthcare Group (NHG, Singapore), and the Nanyang Technological University (NTU Singapore). This work is part of the Ability data project in RRIS (www.ntu.edu.sg/rris/research-focus/ability-data (accessed on 1 June 2022)).