AIMC Topic: Models, Molecular

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A Survey of Deep Learning Methods for Estimating the Accuracy of Protein Quaternary Structure Models.

Biomolecules
The quality prediction of quaternary structure models of a protein complex, in the absence of its true structure, is known as the Estimation of Model Accuracy (EMA). EMA is useful for ranking predicted protein complex structures and using them approp...

Accurate structure prediction of biomolecular interactions with AlphaFold 3.

Nature
The introduction of AlphaFold 2 has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications in protein modelling and design. Here we describe our AlphaFold 3 model with a substantially...

A Multi-view Molecular Pre-training with Generative Contrastive Learning.

Interdisciplinary sciences, computational life sciences
Molecular representation learning can preserve meaningful molecular structures as embedding vectors, which is a necessary prerequisite for molecular property prediction. Yet, learning how to accurately represent molecules remains challenging. Previou...

MolLoG: A Molecular Level Interpretability Model Bridging Local to Global for Predicting Drug Target Interactions.

Journal of chemical information and modeling
Developing new pharmaceuticals is a costly and time-consuming endeavor fraught with significant safety risks. A critical aspect of drug research and disease therapy is discerning the existence of interactions between drugs and proteins. The evolution...

Cryo2StructData: A Large Labeled Cryo-EM Density Map Dataset for AI-based Modeling of Protein Structures.

Scientific data
The advent of single-particle cryo-electron microscopy (cryo-EM) has brought forth a new era of structural biology, enabling the routine determination of large biological molecules and their complexes at atomic resolution. The high-resolution structu...

Reactivities of acrylamide warheads toward cysteine targets: a QM/ML approach to covalent inhibitor design.

Journal of computer-aided molecular design
Covalent inhibition offers many advantages over non-covalent inhibition, but covalent warhead reactivity must be carefully balanced to maintain potency while avoiding unwanted side effects. While warhead reactivities are commonly measured with assays...

Machine Learning-Based Prediction of Reduction Potentials for Pt Complexes.

Journal of chemical information and modeling
Some of the well-known drawbacks of clinically approved Pt complexes can be overcome using six-coordinate Pt complexes as inert prodrugs, which release the corresponding four-coordinate active Pt species upon reduction by cellular reducing agents. Th...

Generative artificial intelligence for de novo protein design.

Current opinion in structural biology
Engineering new molecules with desirable functions and properties has the potential to extend our ability to engineer proteins beyond what nature has so far evolved. Advances in the so-called 'de novo' design problem have recently been brought forwar...

DEBFold: Computational Identification of RNA Secondary Structures for Sequences across Structural Families Using Deep Learning.

Journal of chemical information and modeling
It is now known that RNAs play more active roles in cellular pathways beyond simply serving as transcription templates. These biological mechanisms might be mediated by higher RNA stereo conformations, triggering the need to understand RNA secondary ...

Generalized biomolecular modeling and design with RoseTTAFold All-Atom.

Science (New York, N.Y.)
Deep-learning methods have revolutionized protein structure prediction and design but are presently limited to protein-only systems. We describe RoseTTAFold All-Atom (RFAA), which combines a residue-based representation of amino acids and DNA bases w...