Linguistics

Contains research objectives and summary of research on one research project.National Institute of Mental Health (Grant 3 P01 MH13390-08S1)National Institutes of Health (Grant 5 TOl HD00111-10)National Institute of Mental Health (Grant HD 05168-01, 02, 03)M.I.T. Sloan Fund for Basic ResearchGrant Foundatio

The role of the language production system in shaping grammars.

We argue for an extension of the proposal that grammars are in part shaped by processing systems. Our extension focuses on production, and we use that to explore explanations for certain subject/object asymmetries in extraction structures

Linguistics

Contains research objectives and reports on two research project.National Institute of Mental Health (Grant 5 P01 MH-13390-04

Linguistics

Contains reports on five research projects.National Institute of Mental Health (Grant 5 PO1 MH-13390-04

Linguistics

Contains research objectives and reports on five research projects.National Institutes of Health (Grant 1 P01 MH-13390-02

Communications Biophysics

Contains reports on ten research projects.National Institutes of Health (Grant 5 P01 NS13126)National Institutes of Health (Training Grant 5 T32 NS0704)National Science Foundation (Grant BNS80-06369)National Institutes of Health (Grant 5 R01 NS11153)National Science Foundation (Grant BNS77-16861)National Institutes of Health (Grant 5 RO1 NS12846)National Science Foundation (Grant BNS77-21751)National Institutes of Health (Grant 1 P01 NS14092)Karmazin Foundation through the Council for the Arts at MITNational Institutes of Health (Fellowship 5 F32 NS06386)National Science Foundation (Fellowship SP179-14913)National Institutes of Health (Grant 5 RO1 NS11080

Communications Biophysics

Contains reports on eight research projects split into four sections.National Institutes of Health (Grant 5 P01 NS13126)National Institutes of Health (Grant 5 K04 NS00113)National Institutes of Health (Training Grant 5 T32 NS07047)National Science Foundation (Grant BNS80-06369)National Institutes of Health (Grant 5 ROl NS11153)National Institutes of Health (Fellowship 1 F32 NS06544)National Science Foundation (Grant BNS77-16861)National Institutes of Health (Grant 5 R01 NS10916)National Institutes of Health (Grant 5 RO1 NS12846)National Science Foundation (Grant BNS77-21751)National Institutes of Health (Grant 1 R01 NS14092)National Institutes of Health (Grant 2 R01 NS11680)National Institutes of Health (Grant 5 ROl1 NS11080)National Institutes of Health (Training Grant 5 T32 GM07301

Communications Biophysics

Contains reports on nine research projects split into four sections.National Institutes of Health (Grant 5 P01 NS13126)National Institutes of Health (Grant 5 K04 NS00113)National Institutes of Health (Training Grant 5 T32 NS07047)National Institutes of Health (Grant 5 ROl NS11153-03)National Institutes of Health (Fellowship 1 T32 NS07099-01)National Science Foundation (Grant BNS77-16861)National Institutes of Health (Grant 5 ROl NS10916)National Institutes of Health (Grant 5 ROl NS12846)National Science Foundation (Grant BNS77-21751)National Institutes of Health (Grant 1 RO1 NS14092)Health Sciences FundNational Institutes of Health (Grant 2 R01 NS11680)National Institutes of Health (Grant 2 RO1 NS11080)National Institutes of Health (Training Grant 5 T32 GM07301

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