AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Blurred Target Tracking by Blur-driven Tracker ∗

Visual tracking plays an important role in many computer vision tasks. A common assumption in previous methods is that the video frames are blur free. In reality, motion blurs are pervasive in the real videos. In this paper we present a novel BLUr-driven Tracker (BLUT) framework for tracking motion-blurred targets. BLUT actively uses the information from blurs without performing deblurring. Specifically, we integrate the tracking problem with the motion-from-blur problem under a unified sparse approximation framework. We further use the motion information inferred by blurs to guide the sampling process in the particle filter based tracking. To evaluate our method, we have collected a large number of video sequences with significant motion blurs and compared BLUT with stateof-the-art trackers. Experimental results show that, while many previous methods are sensitive to motion blurs, BLUT can robustly and reliably track severely blurred targets. 1

The Potential and Flux Landscape Theory of Ecology

<div><p>The species in ecosystems are mutually interacting and self sustainable stable for a certain period. Stability and dynamics are crucial for understanding the structure and the function of ecosystems. We developed a potential and flux landscape theory of ecosystems to address these issues. We show that the driving force of the ecological dynamics can be decomposed to the gradient of the potential landscape and the curl probability flux measuring the degree of the breaking down of the detailed balance (due to in or out flow of the energy to the ecosystems). We found that the underlying intrinsic potential landscape is a global Lyapunov function monotonically going down in time and the topology of the landscape provides a quantitative measure for the global stability of the ecosystems. We also quantified the intrinsic energy, the entropy, the free energy and constructed the non-equilibrium thermodynamics for the ecosystems. We studied several typical and important ecological systems: the predation, competition, mutualism and a realistic lynx-snowshoe hare model. Single attractor, multiple attractors and limit cycle attractors emerge from these studies. We studied the stability and robustness of the ecosystems against the perturbations in parameters and the environmental fluctuations. We also found that the kinetic paths between the multiple attractors do not follow the gradient paths of the underlying landscape and are irreversible because of the non-zero flux. This theory provides a novel way for exploring the global stability, function and the robustness of ecosystems.</p></div

The potential landscape, barrier height of the population landscape and the sensitivity of parameters for lynx-snowshoe hare model.

<p>(A) The population potential landscape for lynx-snowshoe hare model. (B) The barrier heights versus changing parameters. The basic set of the parameters are: . (C) The barrier heights versus the hares' rate of population growth.</p

The barrier height of the population landscape, escape time and dissipation rate versus the rate parameters for predation model.

<p>(A) The barrier height of the population landscape versus . (B) The escape time versus barrier height of the population landscape for changing . (C) The dissipation rate versus . (D) The barrier height of the population landscape versus . (E) The escape time versus barrier height of the population landscape for changing . (F) The dissipation rate versus . (G) The barrier height of the population landscape versus . (H) The escape time versus barrier height of the population landscape for changing . (I) The dissipation rate versus .</p

The schematic diagram for the ecological models.

<p>(A)Predation model. (B)Competition model. (C)Mutualism model. The potential landscape is linked with the probability by in species space. (D) Limit cycle attractor. The barrier height from the maximum inside the closed ring to the potential maximum along the ring can quantify the stability of the limit cycle attractor. (E) Multiple attractors. There are four stable states: survival alone state of species , survival alone state of species , coexisting state , and both extinct state . is the saddle points between the attractors and while is the saddle points between the attractors and . The barrier heights from the saddle points to the potential minimums of the basins can quantify the stability of each attractor.</p

The barrier height of intrinsic potential landscape and free energy versus the rate parameters for predation model.

<p>The barrier heights of intrinsic potential landscape versus parameters (A), (B), (C). The free energy versus (D), (E), (F).</p

The barrier height of the population landscape, escape time and dissipation rate versus the rate parameters for competition model.

<p>(A) The barrier height of the population landscape versus . (B) The escape time versus barrier height of the population potential landscape. (C) The dissipation rate versus . (D) The barrier height of the population potential landscape versus . (E) The escape time versus barrier height of the population potential landscape. (F) The dissipation rate versus . (G) The barrier heights of the population landscape versus . (H) The escape time versus barrier height of the population potential landscape. (I) The dissipation rate versus .</p

The barrier height of the population landscape, escape time and dissipation rate versus the diffusion coefficient for mutualism model.

<p>(A) The barrier height of the population landscape versus the diffusion coefficient . (B) The escape time versus the barrier height of the population landscape. (C) The dissipation rate versus the diffusion coefficient .</p

The potential landscapes for the predation, competition and mutualism models.

<p>Top row: The population potential landscape ((A) predation model. (B) competition model. (C) mutualism model.) Purple arrows represent the flux velocity() while the black arrows represent the negative gradient of population potential(). Bottom row: The potential intrinsic energy landscape . ((D) predation model. (E) competition model. (F) mutualism model.). Purple arrows represent the intrinsic flux velocity() while the black arrows represent the negative gradient of intrinsic potential().</p