![]() ![]() Some studies 14, 15, 16, 17 proposed to represent pocket and molecule as 3D graphs and used graph neural networks (GNNs) for encoding and decoding. For example, methods using 3D convolutional neural networks 13 are used to capture 3D inductive bias, but they still struggle to convert atomic density grids into discrete molecules. The increase of 3D protein–ligand complex structures data 10 and advances in geometric deep learning have paved the way for artificial intelligence algorithms to directly design molecules with 3D binding poses 11, 12. However, both representations disregard three-dimensional (3D) spatial interactions, rendering them suboptimal for target-aware molecule generation. Earlier molecular generative models relied on either molecular string representations 2, 3, 4, 5 or graph representations 6, 7, 8, 9. De novo molecule generation using artificial intelligence has recently gained attention as a tool for drug discovery. ![]() Structure-based drug design, which involves designing molecules that can specifically bind to a desired target protein, is a fundamental and challenging drug discovery task 1. Lingo3DMol outperformed state-of-the-art methods in terms of drug likeness, synthetic accessibility, pocket binding mode and molecule generation speed. The Directory of Useful Decoys-Enhanced dataset was used for evaluation. Lingo3DMol can efficiently traverse drug-like chemical spaces, preventing the formation of unusual structures. Additionally, we trained a separate non-covalent interaction predictor to provide essential binding pattern information for the generative model. A new molecular representation, the fragment-based simplified molecular-input line-entry system with local and global coordinates, was developed to assist the model in learning molecular topologies and atomic spatial positions. To address these challenges, we introduce Lingo3DMol, a pocket-based 3D molecule generation method that combines language models and geometric deep learning technology. ![]() Its hypotenuse is T^3, its bigger side is T^2 and its smaller is T^1.Generative models for molecules based on sequential line notation (for example, the simplified molecular-input line-entry system) or graph representation have attracted an increasing interest in the field of structure-based drug design, but they struggle to capture important three-dimensional (3D) spatial interactions and often produce undesirable molecular structures. This orthogonal scalene triangle has all its sides in ratio T and scalene angle ArcTan, T=SQRT. My decoding Plato’s Timaeus “MOST BEAUTIFUL TRIANGLE” shows that Kepler / Magirus Triangle is a similar triangle, “not the same” and” not as beautiful”, but constituent to that of Plato’s: ![]() These familiar triangles are found embodied in pentagrams and Penrose tiles. The isosceles triangle above on the right with a base of 1 two equal sides of Phi is known as a Golden Triangle. Other triangles with Golden Ratio proportions can be created with a Phi (1.618 0339 …) to 1 relationship of the base and sides of triangles: The Kepler triangle is the only right-angle triangle whose side are in a geometric progression: The square root of phi times Φ = 1 and 1 times Φ = Φ.Īlthough difficult to prove with certainty due to deterioration through the ages, this angle is believed by some to have been used by the ancient Egyptians in the construction of the Great Pyramid of Cheops. The Pythagorean 3-4-5 triangle is the only right-angle triangle whose sides are in an arithmetic progression. It has an angle of 51.83 ° (or 51★0′), which has a cosine of 0.618 or phi. ![]()
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