Can Machine Learning Overcome the 95% Failure Rate and Reality that Only 30% of Approved Cancer Drugs Meaningfully Extend Patient Survival?

Despite implementing hundreds of strategies, cancer drug development suffers from a 95% failure rate over 30 years, with only 30% of approved cancer drugs extending patient survival beyond 2.5 months. Adding more criteria without eliminating nonessential ones is impractical and may fall into the "survivorship bias" trap.

Domain-agnostic predictions of nanoscale interactions in proteins and nanoparticles.

Although challenging, the accurate and rapid prediction of nanoscale interactions has broad applications for numerous biological processes and material properties. While several models have been developed to predict the interaction of specific biological components, they use system-specific information that hinders their application to more general materials. Here we present NeCLAS, a general and efficient machine learning pipeline that predicts the location of nanoscale interactions, providing human-intelligible predictions.

Finite-size effects in the static structure factor S(k) and S(0) for a two-dimensional Yukawa liquid.

Finite-size effects in the static structure factor S(k) are analyzed for an amorphous substance. As the number of particles is reduced, S(0) increases greatly, up to an order of magnitude. Meanwhile, there is a decrease in the height of the first peak S_{peak}.

Elucidating the polycyclic aromatic hydrocarbons involved in soot inception.

Polycyclic aromatic hydrocarbons are the main precursors to soot particles in combustion systems. A lack of direct experimental evidence has led to controversial theoretical explanations for the transition from gas-phase species to organic soot clusters. This work focuses on sampling infant soot particles from well-defined flames followed by analysis using state-of-the-art mass spectrometry.

Low-THz Vibrations of Biological Membranes.

A growing body of work has linked key biological activities to the mechanical properties of cellular membranes, and as a means of identification. Here, we present a computational approach to simulate and compare the vibrational spectra in the low-THz region for mammalian and bacterial membranes, investigating the effect of membrane asymmetry and composition, as well as the conserved frequencies of a specific cell. We find that asymmetry does not impact the vibrational spectra, and the impact of sterols depends on the mobility of the components of the membrane.

Comparison of the static structure factor at long wavelengths for a dusty plasma liquid and other liquids.

Especially small values of the static structure factor S(k) at long wavelengths, i.e., small k, were obtained in an analysis of experimental data, for a two-dimensional dusty plasma in its liquid state.

Chiral chromatography and surface chirality of carbon nanoparticles.

Chiral carbon nanoparticles (CNPs) represent a rapidly evolving area of research for optical and biomedical technologies. Similar to small molecules, applications of CNPs as well as fundamental relationships between their optical activity and structural asymmetry would greatly benefit from their enantioselective separations by chromatography. However, this technique remains in its infancy for chiral carbon and other nanoparticles.

Struct2Graph: a graph attention network for structure based predictions of protein-protein interactions.

Background: Development of new methods for analysis of protein-protein interactions (PPIs) at molecular and nanometer scales gives insights into intracellular signaling pathways and will improve understanding of protein functions, as well as other nanoscale structures of biological and abiological origins. Recent advances in computational tools, particularly the ones involving modern deep learning algorithms, have been shown to complement experimental approaches for describing and rationalizing PPIs. However, most of the existing works on PPI predictions use protein-sequence information, and thus have difficulties in accounting for the three-dimensional organization of the protein chains.

Distance-dependent resonance energy transfer in alkyl-terminated Si nanocrystal solids.

Understanding and controlling the energy transfer between silicon nanocrystals is of significant importance for the design of efficient optoelectronic devices. However, previous studies on silicon nanocrystal energy transfer were limited because of the strict requirements to precisely control the inter-dot distance and to perform all measurements in air-free environments to preclude the effect of ambient oxygen. Here, we systematically investigate the distance-dependent resonance energy transfer in alkyl-terminated silicon nanocrystals for the first time.

Unifying structural descriptors for biological and bioinspired nanoscale complexes.

Biomimetic nanoparticles are known to serve as nanoscale adjuvants, enzyme mimics and amyloid fibrillation inhibitors. Their further development requires better understanding of their interactions with proteins. The abundant knowledge about protein-protein interactions can serve as a guide for designing protein-nanoparticle assemblies, but the chemical and biological inputs used in computational packages for protein-protein interactions are not applicable to inorganic nanoparticles.

Stochastic and network analysis of polycyclic aromatic growth in a coflow diffusion flame.

An important step in predicting the growth of soot nanoparticles is understanding how gas phase variations affect the formation of their aromatic precursors. Once formed, these aromatic structures begin to assemble into nanoparticles and, regardless of the clustering process, the molecular properties of the aromatic precursors play an important role. Leveraging existing experimental data collected from a coflow Jet A-1 surrogate diffusion flame, in this paper we report on a detailed study of the spatial evolution of molecular structures of polycyclic aromatic compounds (PACs) and their corresponding formation pathways.

On Drug-Membrane Permeability of Antivirals for SARS-CoV-2.

One of the key parameters required to identify effective drugs is membrane permeability, as a compound intended for an intracellular target with poor permeability will have low efficacy. In this paper, we leverage a computational approach recently developed by our group to study the interactions between nanoparticles and mammalian membranes to study the time of entry of a variety of drugs into the viral envelope of coronavirus as well as cellular organelles. Using a combination of all-atoms molecular dynamics simulations and statistical analysis, we consider both drug characteristics and membrane properties to determine the behavior of 79 drugs and their interactions with the viral envelope, composed of the membrane and spike protein, as well as five other membranes that correspond to various mammalian compartments (lysosome, plasma, Golgi, mitochondrial, and endoplasmic reticulum membranes).

Combustion by-products and their health effects: Summary of the 16th international congress.

The 16th International Congress on Combustion By-Products and their Health Effects (PIC2019) was held in Ann Arbor, Michigan, from July 10 to 12, 2019. For the last 28 years, this conference has served as an interdisciplinary platform for the discussion of the formation, environmental fate, health effects, policy, and remediation of combustion by-products. The technical areas for PIC2019 included mobile and stationary sources in urban environments, open fires, indoor air pollution, and halogenated pollutants.

A deep learning architecture for metabolic pathway prediction.

Motivation: Understanding the mechanisms and structural mappings between molecules and pathway classes are critical for design of reaction predictors for synthesizing new molecules. This article studies the problem of prediction of classes of metabolic pathways (series of chemical reactions occurring within a cell) in which a given biochemical compound participates. We apply a hybrid machine learning approach consisting of graph convolutional networks used to extract molecular shape features as input to a random forest classifier.

Predicting the Time of Entry of Nanoparticles in Lipid Membranes.

The number of engineered nanoparticles for applications in the biomedical arena has grown tremendously over the last years due to advances in the science of synthesis and characterization. For most applications, the crucial step is the transport through a physiological cellular membrane. However, the behavior of nanoparticles in a biological matrix is a very complex problem that depends not only on the type of nanoparticle but also on its size, shape, phase, surface charge, chemical composition, and agglomeration state.

Anti-Biofilm Activity of Graphene Quantum Dots via Self-Assembly with Bacterial Amyloid Proteins.

Bacterial biofilms represent an essential part of Earth's ecosystem that can cause multiple ecological, technological, and health problems. The environmental resilience and sophisticated organization of biofilms are enabled by the extracellular matrix that creates a protective network of biomolecules around the bacterial community. Current anti-biofilm agents can interfere with extracellular matrix production but, being based on small molecules, are degraded by bacteria and rapidly diffuse away from biofilms.

Photoionization Efficiencies of Five Polycyclic Aromatic Hydrocarbons.

We have measured photoionization-efficiency curves for pyrene, fluoranthene, chrysene, perylene, and coronene in the photon energy range of 7.5-10.2 eV and derived their photoionization cross-section curves in this energy range.

Critical Assessment of Photoionization Efficiency Measurements for Characterization of Soot-Precursor Species.

We present a critical evaluation of photoionization efficiency (PIE) measurements coupled with aerosol mass spectrometry for the identification of condensed soot-precursor species extracted from a premixed atmospheric-pressure ethylene/oxygen/nitrogen flame. Definitive identification of isomers by any means is complicated by the large number of potential isomers at masses likely to comprise particles at flame temperatures. This problem is compounded using PIE measurements by the similarity in ionization energies and PIE-curve shapes among many of these isomers.

Chiral Graphene Quantum Dots.

Chiral nanostructures from metals and semiconductors attract wide interest as components for polarization-enabled optoelectronic devices. Similarly to other fields of nanotechnology, graphene-based materials can greatly enrich physical and chemical phenomena associated with optical and electronic properties of chiral nanostructures and facilitate their applications in biology as well as other areas. Here, we report that covalent attachment of l/d-cysteine moieties to the edges of graphene quantum dots (GQDs) leads to their helical buckling due to chiral interactions at the "crowded" edges.

Photoionization mass spectrometric measurements of initial reaction pathways in low-temperature oxidation of 2,5-dimethylhexane.

Product formation from R + O2 reactions relevant to low-temperature autoignition chemistry was studied for 2,5-dimethylhexane, a symmetrically branched octane isomer, at 550 and 650 K using Cl-atom initiated oxidation and multiplexed photoionization mass spectrometry (MPIMS). Interpretation of time- and photon-energy-resolved mass spectra led to three specific results important to characterizing the initial oxidation steps: (1) quantified isomer-resolved branching ratios for HO2 + alkene channels; (2) 2,2,5,5-tetramethyltetrahydrofuran is formed in substantial yield from addition of O2 to tertiary 2,5-dimethylhex-2-yl followed by isomerization of the resulting ROO adduct to tertiary hydroperoxyalkyl (QOOH) and exhibits a positive dependence on temperature over the range covered leading to a higher flux relative to aggregate cyclic ether yield. The higher relative flux is explained by a 1,5-hydrogen atom shift reaction that converts the initial primary alkyl radical (2,5-dimethylhex-1-yl) to the tertiary alkyl radical 2,5-dimethylhex-2-yl, providing an additional source of tertiary alkyl radicals.

Stochastic atomistic simulation of polycyclic aromatic hydrocarbon growth in combustion.

Nanoparticles formed in gas phase environments, such as combustion, have an important impact on society both as engineering components and hazardous pollutants. A new software package, the Stochastic Nanoparticle Simulator (SNAPS) was developed, applying a stochastic chemical kinetics methodology, to computationally investigate the growth of nanoparticle precursors through trajectories of chemical reactions. SNAPS was applied to characterize the growth of polycyclic aromatic hydrocarbons (PAHs), important precursors of carbonaceous nanoparticles and soot, in a premixed laminar benzene flame, using a concurrently developed PAH growth chemical reaction mechanism, as well as an existing benzene oxidation mechanism.

Ab initio investigation of the thermal decomposition of n-butylcyclohexane.

Environmental and energy security concerns have motivated an increased focus on developing clean, efficient combustors, which increasingly relies on insight into the combustion chemistry of fuels. In particular, naphthenes (cycloalkanes and alkylcycloalkanes) are important chemical components of distillate fuels, such as diesel and jet fuels. As such, there is a growing interest in describing napthene reactivity with kinetic mechanisms.

Reaction pathways for the thermal decomposition of methyl butanoate.

In recent years, biodiesel fuels, consisting of long-chain alkyl (methyl, ethyl, propyl) esters, have emerged as viable alternatives to petroleum-based fuels. From a combustion chemistry standpoint, there is great interest in developing accurate reaction models for these new molecules that can be used to predict their behaviors in various regimes. In this paper, we report a detailed study of the unimolecular decomposition pathways of methyl butanoate (MB), a short-chain ester that contains the basic chemical structure of biodiesel fuels.

Free energy calculation of permeant-membrane interactions using molecular dynamics simulations.

Nanotoxicology, the science concerned with the safe use of nanotechnology and nanostructure design for biological applications, is a field of research that has recently received great attention, as a result of the rapid growth in nanotechnology. Many nanostructures are of a scale and chemical composition similar to many biomolecular environments, and recent papers have reported evident toxicity of selected nanoparticles. Molecular simulations can help develop a mechanistic understanding of how structural properties affect bioactivity.